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Yuan T, Dong J, Jia B, Jiang H, Zhao Z, Zhou M. DTDHM: detection of tandem duplications based on hybrid methods using next-generation sequencing data. PeerJ 2024; 12:e17748. [PMID: 39076774 PMCID: PMC11285389 DOI: 10.7717/peerj.17748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 06/24/2024] [Indexed: 07/31/2024] Open
Abstract
Background Tandem duplication (TD) is a common and important type of structural variation in the human genome. TDs have been shown to play an essential role in many diseases, including cancer. However, it is difficult to accurately detect TDs due to the uneven distribution of reads and the inherent complexity of next-generation sequencing (NGS) data. Methods This article proposes a method called DTDHM (detection of tandem duplications based on hybrid methods), which utilizes NGS data to detect TDs in a single sample. DTDHM builds a pipeline that integrates read depth (RD), split read (SR), and paired-end mapping (PEM) signals. To solve the problem of uneven distribution of normal and abnormal samples, DTDHM uses the K-nearest neighbor (KNN) algorithm for multi-feature classification prediction. Then, the qualified split reads and discordant reads are extracted and analyzed to achieve accurate localization of variation sites. This article compares DTDHM with three other methods on 450 simulated datasets and five real datasets. Results In 450 simulated data samples, DTDHM consistently maintained the highest F1-score. The average F1-score of DTDHM, SVIM, TARDIS, and TIDDIT were 80.0%, 56.2%, 43.4%, and 67.1%, respectively. The F1-score of DTDHM had a small variation range and its detection effect was the most stable and 1.2 times that of the suboptimal method. Most of the boundary biases of DTDHM fluctuated around 20 bp, and its boundary deviation detection ability was better than TARDIS and TIDDIT. In real data experiments, five real sequencing samples (NA19238, NA19239, NA19240, HG00266, and NA12891) were used to test DTDHM. The results showed that DTDHM had the highest overlap density score (ODS) and F1-score of the four methods. Conclusions Compared with the other three methods, DTDHM achieved excellent results in terms of sensitivity, precision, F1-score, and boundary bias. These results indicate that DTDHM can be used as a reliable tool for detecting TDs from NGS data, especially in the case of low coverage depth and tumor purity samples.
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Affiliation(s)
- Tianting Yuan
- School of Computer Science and Technology, Liaocheng University, Liaocheng, China
| | - Jinxin Dong
- School of Computer Science and Technology, Liaocheng University, Liaocheng, China
| | - Baoxian Jia
- School of Computer Science and Technology, Liaocheng University, Liaocheng, China
| | - Hua Jiang
- School of Computer Science and Technology, Liaocheng University, Liaocheng, China
| | - Zuyao Zhao
- Orthopedics Department, Liaocheng People’s Hospital, Liaocheng, China
| | - Mengjiao Zhou
- School of Computer Science and Technology, Liaocheng University, Liaocheng, China
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2
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Xie K, Ge X, Alvi HAK, Liu K, Song J, Yu Q. OTSUCNV: an adaptive segmentation and OTSU-based anomaly classification method for CNV detection using NGS data. BMC Genomics 2024; 25:126. [PMID: 38291375 PMCID: PMC10826217 DOI: 10.1186/s12864-024-10018-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 01/15/2024] [Indexed: 02/01/2024] Open
Abstract
Copy-number variations (CNVs), which refer to deletions and duplications of chromosomal segments, represent a significant source of variation among individuals, contributing to human evolution and being implicated in various diseases ranging from mental illness and developmental disorders to cancer. Despite the development of several methods for detecting copy number variations based on next-generation sequencing (NGS) data, achieving robust detection performance for CNVs with arbitrary coverage and amplitude remains challenging due to the inherent complexity of sequencing samples. In this paper, we propose an alternative method called OTSUCNV for CNV detection on whole genome sequencing (WGS) data. This method utilizes a newly designed adaptive sequence segmentation algorithm and an OTSU-based CNV prediction algorithm, which does not rely on any distribution assumptions or involve complex outlier factor calculations. As a result, the effective detection of CNVs is achieved with lower computational complexity. The experimental results indicate that the proposed method demonstrates outstanding performance, and hence it may be used as an effective tool for CNV detection.
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Affiliation(s)
- Kun Xie
- School of Computer Science and Technology, Xidian University, Xi'an, 710071, China
| | - Xiaojun Ge
- School of Computer Science and Technology, Xidian University, Xi'an, 710071, China
| | - Haque A K Alvi
- School of Computer Science and Technology, Xidian University, Xi'an, 710071, China
| | - Kang Liu
- School of Computer Science and Technology, Xidian University, Xi'an, 710071, China
| | - Jianfeng Song
- School of Computer Science and Technology, Xidian University, Xi'an, 710071, China.
| | - Qiang Yu
- School of Computer Science and Technology, Xidian University, Xi'an, 710071, China.
- Hangzhou Institute of Technology, Xidian University, Hangzhou, 311200, China.
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3
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Hoskins I, Sun S, Cote A, Roth FP, Cenik C. satmut_utils: a simulation and variant calling package for multiplexed assays of variant effect. Genome Biol 2023; 24:82. [PMID: 37081510 PMCID: PMC10116734 DOI: 10.1186/s13059-023-02922-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 04/04/2023] [Indexed: 04/22/2023] Open
Abstract
The impact of millions of individual genetic variants on molecular phenotypes in coding sequences remains unknown. Multiplexed assays of variant effect (MAVEs) are scalable methods to annotate relevant variants, but existing software lacks standardization, requires cumbersome configuration, and does not scale to large targets. We present satmut_utils as a flexible solution for simulation and variant quantification. We then benchmark MAVE software using simulated and real MAVE data. We finally determine mRNA abundance for thousands of cystathionine beta-synthase variants using two experimental methods. The satmut_utils package enables high-performance analysis of MAVEs and reveals the capability of variants to alter mRNA abundance.
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Affiliation(s)
- Ian Hoskins
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, 78712, USA
| | - Song Sun
- The Donnelly Centre and Departments of Molecular Genetics and Computer Science, University of Toronto, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada
| | - Atina Cote
- The Donnelly Centre and Departments of Molecular Genetics and Computer Science, University of Toronto, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada
| | - Frederick P Roth
- The Donnelly Centre and Departments of Molecular Genetics and Computer Science, University of Toronto, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada
| | - Can Cenik
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, 78712, USA.
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4
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Duncavage EJ, Coleman JF, de Baca ME, Kadri S, Leon A, Routbort M, Roy S, Suarez CJ, Vanderbilt C, Zook JM. Recommendations for the Use of in Silico Approaches for Next-Generation Sequencing Bioinformatic Pipeline Validation: A Joint Report of the Association for Molecular Pathology, Association for Pathology Informatics, and College of American Pathologists. J Mol Diagn 2023; 25:3-16. [PMID: 36244574 DOI: 10.1016/j.jmoldx.2022.09.007] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 09/14/2022] [Accepted: 09/28/2022] [Indexed: 11/21/2022] Open
Abstract
In silico approaches for next-generation sequencing (NGS) data modeling have utility in the clinical laboratory as a tool for clinical assay validation. In silico NGS data can take a variety of forms, including pure simulated data or manipulated data files in which variants are inserted into existing data files. In silico data enable simulation of a range of variants that may be difficult to obtain from a single physical sample. Such data allow laboratories to more accurately test the performance of clinical bioinformatics pipelines without sequencing additional cases. For example, clinical laboratories may use in silico data to simulate low variant allele fraction variants to test the analytical sensitivity of variant calling software or simulate a range of insertion/deletion sizes to determine the performance of insertion/deletion calling software. In this article, the Working Group reviews the different types of in silico data with their strengths and limitations, methods to generate in silico data, and how data can be used in the clinical molecular diagnostic laboratory. Survey data indicate how in silico NGS data are currently being used. Finally, potential applications for which in silico data may become useful in the future are presented.
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Affiliation(s)
- Eric J Duncavage
- In Silico Pipeline Validation Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri.
| | - Joshua F Coleman
- In Silico Pipeline Validation Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology, University of Utah, Salt Lake City, Utah
| | - Monica E de Baca
- In Silico Pipeline Validation Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Pacific Pathology Partners, Seattle, Washington
| | - Sabah Kadri
- In Silico Pipeline Validation Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology, Anne and Robert H Lurie Children's Hospital of Chicago, Chicago, Illinois
| | - Annette Leon
- In Silico Pipeline Validation Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Color Health, Burlingame, California
| | - Mark Routbort
- In Silico Pipeline Validation Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Hematopathology, MD Anderson Cancer Center, Houston, Texas
| | - Somak Roy
- In Silico Pipeline Validation Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology and Laboratory Medicine, Cincinnati Children's Hospital, Cincinnati, Ohio
| | - Carlos J Suarez
- In Silico Pipeline Validation Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology, Stanford University, Palo Alto, California
| | - Chad Vanderbilt
- In Silico Pipeline Validation Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Justin M Zook
- In Silico Pipeline Validation Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Biomarker and Genomic Sciences Group, National Institute of Standards and Technology, Gaithersburg, Maryland
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Zhang T, Dong J, Jiang H, Zhao Z, Zhou M, Yuan T. CNV-PCC: An efficient method for detecting copy number variations from next-generation sequencing data. Front Bioeng Biotechnol 2022; 10:1000638. [DOI: 10.3389/fbioe.2022.1000638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 11/18/2022] [Indexed: 12/03/2022] Open
Abstract
Copy number variations (CNVs) significantly influence the diversity of the human genome and the occurrence of many complex diseases. The next-generation sequencing (NGS) technology provides rich data for detecting CNVs, and the read depth (RD)-based approach is widely used. However, low CN (copy number of 3–4) duplication events are challenging to identify with existing methods, especially when the size of CNVs is small. In addition, the RD-based approach can only obtain rough breakpoints. We propose a new method, CNV-PCC (detection of CNVs based on Principal Component Classifier), to identify CNVs in whole genome sequencing data. CNV-PPC first uses the split read signal to search for potential breakpoints. A two-stage segmentation strategy is then implemented to enhance the identification capabilities of low CN duplications and small CNVs. Next, the outlier scores are calculated for each segment by PCC (Principal Component Classifier). Finally, the OTSU algorithm calculates the threshold to determine the CNVs regions. The analysis of simulated data results indicates that CNV-PCC outperforms the other methods for sensitivity and F1-score and improves breakpoint accuracy. Furthermore, CNV-PCC shows high consistency on real sequencing samples with other methods. This study demonstrates that CNV-PCC is an effective method for detecting CNVs, even for low CN duplications and small CNVs.
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6
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svBreak: A New Approach for the Detection of Structural Variant Breakpoints Based on Convolutional Neural Network. BIOMED RESEARCH INTERNATIONAL 2022; 2022:7196040. [PMID: 35345526 PMCID: PMC8957449 DOI: 10.1155/2022/7196040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 01/04/2022] [Accepted: 01/27/2022] [Indexed: 12/01/2022]
Abstract
Structural variation (SV) is an important type of genome variation and confers susceptibility to human cancer diseases. Systematic analysis of SVs has become a crucial step for the exploration of mechanisms and precision diagnosis of cancers. The central point is how to accurately detect SV breakpoints by using next-generation sequencing (NGS) data. Due to the cooccurrence of multiple types of SVs in the human genome and the intrinsic complexity of SVs, the discrimination of SV breakpoint types is a challenging task. In this paper, we propose a convolutional neural network- (CNN-) based approach, called svBreak, for the detection and discrimination of common types of SV breakpoints. The principle of svBreak is that it extracts a set of SV-related features for each genome site from the sequencing reads aligned to the reference genome and establishes a data matrix where each row represents one site and each column represents one feature and then adopts a CNN model to analyze such data matrix for the prediction of SV breakpoints. The performance of the proposed approach is tested via simulation studies and application to a real sequencing sample. The experimental results demonstrate the merits of the proposed approach when compared with existing methods. Thus, svBreak can be expected to be a supplementary approach in the field of SV analysis in human tumor genomes.
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7
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Identification of Copy Number Alterations from Next-Generation Sequencing Data. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1361:55-74. [DOI: 10.1007/978-3-030-91836-1_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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8
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MacDonald ML, Lee KH. EvalDNA: a machine learning-based tool for the comprehensive evaluation of mammalian genome assembly quality. BMC Bioinformatics 2021; 22:570. [PMID: 34837948 PMCID: PMC8627028 DOI: 10.1186/s12859-021-04480-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Accepted: 11/15/2021] [Indexed: 11/16/2022] Open
Abstract
Background To select the most complete, continuous, and accurate assembly for an organism of interest, comprehensive quality assessment of assemblies is necessary. We present a novel tool, called Evaluation of De Novo Assemblies (EvalDNA), which uses supervised machine learning for the quality scoring of genome assemblies and does not require an existing reference genome for accuracy assessment. Results EvalDNA calculates a list of quality metrics from an assembled sequence and applies a model created from supervised machine learning methods to integrate various metrics into a comprehensive quality score. A well-tested, accurate model for scoring mammalian genome sequences is provided as part of EvalDNA. This random forest regression model evaluates an assembled sequence based on continuity, completeness, and accuracy, and was able to explain 86% of the variation in reference-based quality scores within the testing data. EvalDNA was applied to human chromosome 14 assemblies from the GAGE study to rank genome assemblers and to compare EvalDNA to two other quality evaluation tools. In addition, EvalDNA was used to evaluate several genome assemblies of the Chinese hamster genome to help establish a better reference genome for the biopharmaceutical manufacturing community. EvalDNA was also used to assess more recent human assemblies from the QUAST-LG study completed in 2018, and its ability to score bacterial genomes was examined through application on bacterial assemblies from the GAGE-B study. Conclusions EvalDNA enables scientists to easily identify the best available genome assembly for their organism of interest without requiring a reference assembly. EvalDNA sets itself apart from other quality assessment tools by producing a quality score that enables direct comparison among assemblies from different species. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04480-2.
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Affiliation(s)
- Madolyn L MacDonald
- Center for Bioinformatics and Computational Biology, University of Delaware, 15 Innovation Way, Newark, 19711, USA.,Department of Computer and Information Sciences, University of Delaware, 18 Amstel Ave., Newark, 19716, USA.,Delaware Biotechnology Institute, University of Delaware, 15 Innovation Way, Newark, 19711, USA
| | - Kelvin H Lee
- Delaware Biotechnology Institute, University of Delaware, 15 Innovation Way, Newark, 19711, USA. .,Department of Chemical and Biomolecular Engineering, University of Delaware, 150 Academy Street, Newark, 19716, USA.
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9
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Yuan X, Ma C, Zhao H, Yang L, Wang S, Xi J. STIC: Predicting Single Nucleotide Variants and Tumor Purity in Cancer Genome. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2692-2701. [PMID: 32086221 DOI: 10.1109/tcbb.2020.2975181] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Single nucleotide variant (SNV) plays an important role in cellular proliferation and tumorigenesis in various types of human cancer. Next-generation sequencing (NGS) has provided high-throughput data at an unprecedented resolution to predict SNVs. Currently, there exist many computational methods for either germline or somatic SNV discovery from NGS data, but very few of them are versatile enough to adapt to any situations. In the absence of matched normal samples, the prediction of somatic SNVs from single-tumor samples becomes considerably challenging, especially when the tumor purity is unknown. Here, we propose a new approach, STIC, to predict somatic SNVs and estimate tumor purity from NGS data without matched normal samples. The main features of STIC include: (1) extracting a set of SNV-relevant features on each site and training the BP neural network algorithm on the features to predict SNVs; (2) creating an iterative process to distinguish somatic SNVs from germline ones by disturbing allele frequency; and (3) establishing a reasonable relationship between tumor purity and allele frequencies of somatic SNVs to accurately estimate the purity. We quantitatively evaluate the performance of STIC on both simulation and real sequencing datasets, the results of which indicate that STIC outperforms competing methods.
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10
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Wei L, Dugas M, Sandmann S. SimFFPE and FilterFFPE: improving structural variant calling in FFPE samples. Gigascience 2021; 10:giab065. [PMID: 34553214 PMCID: PMC8458033 DOI: 10.1093/gigascience/giab065] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 07/19/2021] [Accepted: 09/06/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Artifact chimeric reads are enriched in next-generation sequencing data generated from formalin-fixed paraffin-embedded (FFPE) samples. Previous work indicated that these reads are characterized by erroneous split-read support that is interpreted as evidence of structural variants. Thus, a large number of false-positive structural variants are detected. To our knowledge, no tool is currently available to specifically call or filter structural variants in FFPE samples. To overcome this gap, we developed 2 R packages: SimFFPE and FilterFFPE. RESULTS SimFFPE is a read simulator, specifically designed for next-generation sequencing data from FFPE samples. A mixture of characteristic artifact chimeric reads, as well as normal reads, is generated. FilterFFPE is a filtration algorithm, removing artifact chimeric reads from sequencing data while keeping real chimeric reads. To evaluate the performance of FilterFFPE, we performed structural variant calling with 3 common tools (Delly, Lumpy, and Manta) with and without prior filtration with FilterFFPE. After applying FilterFFPE, the mean positive predictive value improved from 0.27 to 0.48 in simulated samples and from 0.11 to 0.27 in real samples, while sensitivity remained basically unchanged or even slightly increased. CONCLUSIONS FilterFFPE improves the performance of SV calling in FFPE samples. It was validated by analysis of simulated and real data.
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Affiliation(s)
- Lanying Wei
- Institute of Medical Informatics, University of Münster, Münster 48149, Germany
| | - Martin Dugas
- Institute of Medical Informatics, University of Münster, Münster 48149, Germany
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg 69120, Germany
| | - Sarah Sandmann
- Institute of Medical Informatics, University of Münster, Münster 48149, Germany
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Yuan X, Xu X, Zhao H, Duan J. ERINS: Novel Sequence Insertion Detection by Constructing an Extended Reference. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1893-1901. [PMID: 31751246 DOI: 10.1109/tcbb.2019.2954315] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Next generation sequencing technology has led to the development of methods for the detection of novel sequence insertions (nsINS). Multiple signatures from short reads are usually extracted to improve nsINS detection performance. However, characterization of nsINSs larger than the mean insert size is still challenging. This article presents a new method, ERINS, to detect nsINS contents and genotypes of full spectrum range size. It integrates the features of structural variations and mapping states of split reads to find nsINS breakpoints, and then adopts a left-most mapping strategy to infer nsINS content by iteratively extending the standard reference at each breakpoint. Finally, it realigns all reads to the extended reference and infers nsINS genotypes through statistical testing on read counts. We test and validate the performance of ERINS on simulation and real sequencing datasets. The simulation experimental results demonstrate that it outperforms several peer methods with respect to sensitivity and precision. The real data application indicates that ERINS obtains high consistent results with those of previously reported and detects nsINSs over 200 base pairs that many other methods fail. In conclusion, ERINS can be used as a supplement to existing tools and will become a routine approach for characterizing nsINSs.
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12
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Alanazi KM, Ali MA, Kim SY, Rahman MO, Farah MA, Alhemaid F, Elangbam M, Gurung AB, Lee J. The cp genome characterization of Adenium obesum: Gene content, repeat organization and phylogeny. Saudi J Biol Sci 2021; 28:3768-3775. [PMID: 34220230 PMCID: PMC8241589 DOI: 10.1016/j.sjbs.2021.03.048] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 03/15/2021] [Accepted: 03/16/2021] [Indexed: 11/01/2022] Open
Abstract
Adenium obesum (Forssk.) Roem. & Schult. belonging to the family Apocynaceae, is remarkable for its horticultural and ornamental values, poisonous nature, and medicinal uses. In order to have understanding of cp genome characterization of highly valued medicinal plant, and the evolutionary and systematic relationships, the complete plastome / chloroplast (cp) genome of A. obesum was sequenced. The assembled cp genome of A. obesum was found to be 154,437 bp, with an overall GC content of 38.1%. A total of 127 unique coding genes were annotated including 96 protein-coding genes, 28 tRNA genes, and 3 rRNA genes. The repeat structures were found to comprise of only mononucleotide repeats. The SSR loci are compososed of only A/T bases. The phylogenetic analysis of cp genomes revealed its proximity with Nerium oleander.
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Affiliation(s)
- Khalid Mashay Alanazi
- Genetics Laboratory, Department of Zoology, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
| | - Mohammad Ajmal Ali
- Department of Botany and Microbiology, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
| | - Soo-Yong Kim
- International Biological Material Research Center, Korea Research Institute of Bioscience and Biotechnology, 125 Gwahangno, Yuseong-gu, Daejeon 34141, Republic of Korea
| | - M Oliur Rahman
- Department of Botany, University of Dhaka, Dhaka 1000, Bangladesh
| | - Mohammad Abul Farah
- Genetics Laboratory, Department of Zoology, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
| | - Fahad Alhemaid
- Department of Botany and Microbiology, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
| | - Meena Elangbam
- Genetics Laboratory, Centre of Advanced Studies in Life Sciences, Manipur University, Canchipur 795 003, India
| | - Arun Bahadur Gurung
- Department of Basic Sciences and Social Sciences, North-Eastern Hill University, Shillong - 793022, Meghalaya, India
| | - Joongku Lee
- Department of Environment and Forest Resources, Chungnam National University, Daehak-ro, Yuseong-gu, Daejeon 34134, Republic of Korea
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Mao YF, Yuan XG, Cun YP. A novel machine learning approach (svmSomatic) to distinguish somatic and germline mutations using next-generation sequencing data. Zool Res 2021; 42:246-249. [PMID: 33709636 PMCID: PMC7995270 DOI: 10.24272/j.issn.2095-8137.2021.014] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 03/10/2021] [Indexed: 11/07/2022] Open
Abstract
Somatic mutations are a large category of genetic variations, which play an essential role in tumorigenesis. Detection of somatic single nucleotide variants (SNVs) could facilitate downstream analysis of tumorigenesis. Many computational methods have been developed to detect SNVs, but most require normal matched samples to differentiate somatic SNVs from the normal state, which can be difficult to obtain. Therefore, developing new approaches for detecting somatic SNVs without matched samples are crucial. In this work, we detected somatic mutations from individual tumor samples based on a novel machine learning approach, svmSomatic, using next-generation sequencing (NGS) data. In addition, as somatic SNV detection can be impacted by multiple mutations, with germline mutations and co-occurrence of copy number variations (CNVs) common in organisms, we used the novel approach to distinguish somatic and germline mutations based on the NGS data from individual tumor samples. In summary, svmSomatic: (1) considers the influence of CNV co-occurrence in detecting somatic mutations; and (2) trains a support vector machine algorithm to distinguish between somatic and germline mutations, without requiring normal matched samples. We further tested and compared svmSomatic with other common methods. Results showed that svmSomatic performance, as measured by F1-score, was significantly better than that of others using both simulation and real NGS data.
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Affiliation(s)
- Yu-Fang Mao
- School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China
| | - Xi-Guo Yuan
- School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China. E-mail:
| | - Yu-Peng Cun
- iFlora Bioinformatics Center, Germplasm Bank of Wild Species, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, Yunnan 650201, China. E-mail:
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14
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Hawari MA, Hong CS, Biesecker LG. SomatoSim: precision simulation of somatic single nucleotide variants. BMC Bioinformatics 2021; 22:109. [PMID: 33676403 PMCID: PMC7936459 DOI: 10.1186/s12859-021-04024-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 02/14/2021] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Somatic single nucleotide variants have gained increased attention because of their role in cancer development and the widespread use of high-throughput sequencing techniques. The necessity to accurately identify these variants in sequencing data has led to a proliferation of somatic variant calling tools. Additionally, the use of simulated data to assess the performance of these tools has become common practice, as there is no gold standard dataset for benchmarking performance. However, many existing somatic variant simulation tools are limited because they rely on generating entirely synthetic reads derived from a reference genome or because they do not allow for the precise customizability that would enable a more focused understanding of single nucleotide variant calling performance. RESULTS SomatoSim is a tool that lets users simulate somatic single nucleotide variants in sequence alignment map (SAM/BAM) files with full control of the specific variant positions, number of variants, variant allele fractions, depth of coverage, read quality, and base quality, among other parameters. SomatoSim accomplishes this through a three-stage process: variant selection, where candidate positions are selected for simulation, variant simulation, where reads are selected and mutated, and variant evaluation, where SomatoSim summarizes the simulation results. CONCLUSIONS SomatoSim is a user-friendly tool that offers a high level of customizability for simulating somatic single nucleotide variants. SomatoSim is available at https://github.com/BieseckerLab/SomatoSim .
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Affiliation(s)
- Marwan A Hawari
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Celine S Hong
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
| | - Leslie G Biesecker
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
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15
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Ali MA. Phylotranscriptomic analysis of Dillenia indica L. (Dilleniales, Dilleniaceae) and its systematics implication. Saudi J Biol Sci 2021; 28:1557-1560. [PMID: 33732040 PMCID: PMC7938110 DOI: 10.1016/j.sjbs.2021.01.038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 01/17/2021] [Accepted: 01/18/2021] [Indexed: 11/21/2022] Open
Abstract
The recent massive development in the next-generation sequencing platforms and bioinformatics tools including cloud based computing have proven extremely useful in understanding the deeper-level phylogenetic relationships of angiosperms. The present phylotranscriptomic analyses address the poorly known evolutionary relationships of the order Dilleniales to order of the other angiosperms using the minimum evolution method. The analyses revealed the nesting of the representative taxon of Dilleniales in the MPT but distinct from the representative of the order Santalales, Caryophyllales, Asterales, Cornales, Ericales, Lamiales, Saxifragales, Fabales, Malvales, Vitales and Berberidopsidales.
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Affiliation(s)
- Mohammad Ajmal Ali
- Department of Botany and Microbiology, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
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16
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Alosaimi S, Bandiang A, van Biljon N, Awany D, Thami PK, Tchamga MSS, Kiran A, Messaoud O, Hassan RIM, Mugo J, Ahmed A, Bope CD, Allali I, Mazandu GK, Mulder NJ, Chimusa ER. A broad survey of DNA sequence data simulation tools. Brief Funct Genomics 2020; 19:49-59. [PMID: 31867604 DOI: 10.1093/bfgp/elz033] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Revised: 10/27/2019] [Accepted: 11/04/2019] [Indexed: 11/12/2022] Open
Abstract
In silico DNA sequence generation is a powerful technology to evaluate and validate bioinformatics tools, and accordingly more than 35 DNA sequence simulation tools have been developed. With such a diverse array of tools to choose from, an important question is: Which tool should be used for a desired outcome? This question is largely unanswered as documentation for many of these DNA simulation tools is sparse. To address this, we performed a review of DNA sequence simulation tools developed to date and evaluated 20 state-of-art DNA sequence simulation tools on their ability to produce accurate reads based on their implemented sequence error model. We provide a succinct description of each tool and suggest which tool is most appropriate for the given different scenarios. Given the multitude of similar yet non-identical tools, researchers can use this review as a guide to inform their choice of DNA sequence simulation tool. This paves the way towards assessing existing tools in a unified framework, as well as enabling different simulation scenario analysis within the same framework.
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Affiliation(s)
- Shatha Alosaimi
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Armand Bandiang
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Noelle van Biljon
- Computational Biology Division, Department of Integrative Biomedical Sciences, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Denis Awany
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Prisca K Thami
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa.,Botswana Harvard AIDS Institute Partnership, Gaborone, Botswana
| | - Milaine S S Tchamga
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Anmol Kiran
- Malawi-Liverpool-Wellcome Trust Clinical Research Programme, Blantyre, Malawi.,Edinburgh University, Edinburgh, UK
| | - Olfa Messaoud
- Université de Tunis El Manar, Institut Pasteur de Tunis, LR16IPT05 Génomique Biomédicale et Oncogénétique, Tunis, 1002, Tunisia
| | - Radia Ismaeel Mohammed Hassan
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Jacquiline Mugo
- Computational Biology Division, Department of Integrative Biomedical Sciences, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Azza Ahmed
- Centre for Bioinformatics and Systems Biology, Faculty of Science, University of Khartoum, Sudan
| | - Christian D Bope
- Computational Biology Division, Department of Integrative Biomedical Sciences, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Imane Allali
- Computational Biology Division, Department of Integrative Biomedical Sciences, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Gaston K Mazandu
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa.,Computational Biology Division, Department of Integrative Biomedical Sciences, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa.,African Institute for Mathematical Sciences (AIMS), Cape Town, South Africa
| | - Nicola J Mulder
- Computational Biology Division, Department of Integrative Biomedical Sciences, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Emile R Chimusa
- Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
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17
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Dong J, Qi M, Wang S, Yuan X. DINTD: Detection and Inference of Tandem Duplications From Short Sequencing Reads. Front Genet 2020; 11:924. [PMID: 32849857 PMCID: PMC7433346 DOI: 10.3389/fgene.2020.00924] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Accepted: 07/24/2020] [Indexed: 11/21/2022] Open
Abstract
Tandem duplication (TD) is an important type of structural variation (SV) in the human genome and has biological significance for human cancer evolution and tumor genesis. Accurate and reliable detection of TDs plays an important role in advancing early detection, diagnosis, and treatment of disease. The advent of next-generation sequencing technologies has made it possible for the study of TDs. However, detection is still challenging due to the uneven distribution of reads and the uncertain amplitude of TD regions. In this paper, we present a new method, DINTD (Detection and INference of Tandem Duplications), to detect and infer TDs using short sequencing reads. The major principle of the proposed method is that it first extracts read depth and mapping quality signals, then uses the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm to find the possible TD regions. The total variation penalized least squares model is fitted with read depth and mapping quality signals to denoise signals. A 2D binary search tree is used to search the neighbor points effectively. To further identify the exact breakpoints of the TD regions, split-read signals are integrated into DINTD. The experimental results of DINTD on simulated data sets showed that DINTD can outperform other methods for sensitivity, precision, F1-score, and boundary bias. DINTD is further validated on real samples, and the experiment results indicate that it is consistent with other methods. This study indicates that DINTD can be used as an effective tool for detecting TDs.
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Affiliation(s)
- Jinxin Dong
- School of Computer Science and Technology, Xidian University, Xi'an, China.,School of Computer Science and Technology, Liaocheng University, Liaocheng, China
| | - Minyong Qi
- School of Computer Science and Technology, Xidian University, Xi'an, China.,School of Computer Science and Technology, Liaocheng University, Liaocheng, China
| | - Shaoqiang Wang
- School of Computer Science and Technology, Xidian University, Xi'an, China
| | - Xiguo Yuan
- School of Computer Science and Technology, Xidian University, Xi'an, China
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18
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Yu Z, Du F, Ban R, Zhang Y. SimuSCoP: reliably simulate Illumina sequencing data based on position and context dependent profiles. BMC Bioinformatics 2020; 21:331. [PMID: 32703148 PMCID: PMC7379788 DOI: 10.1186/s12859-020-03665-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2018] [Accepted: 07/16/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND A number of simulators have been developed for emulating next-generation sequencing data by incorporating known errors such as base substitutions and indels. However, their practicality may be degraded by functional and runtime limitations. Particularly, the positional and genomic contextual information is not effectively utilized for reliably characterizing base substitution patterns, as well as the positional and contextual difference of Phred quality scores is not fully investigated. Thus, a more effective and efficient bioinformatics tool is sorely required. RESULTS Here, we introduce a novel tool, SimuSCoP, to reliably emulate complex DNA sequencing data. The base substitution patterns and the statistical behavior of quality scores in Illumina sequencing data are fully explored and integrated into the simulation model for reliably emulating datasets for different applications. In addition, an integrated and easy-to-use pipeline is employed in SimuSCoP to facilitate end-to-end simulation of complex samples, and high runtime efficiency is achieved by implementing the tool to run in multithreading with low memory consumption. These features enable SimuSCoP to gets substantial improvements in reliability, functionality, practicality and runtime efficiency. The tool is comprehensively evaluated in multiple aspects including consistency of profiles, simulation of genomic variations and complex tumor samples, and the results demonstrate the advantages of SimuSCoP over existing tools. CONCLUSIONS SimuSCoP, a new bioinformatics tool is developed to learn informative profiles from real sequencing data and reliably mimic complex data by introducing various genomic variations. We believe that the presented work will catalyse new development of downstream bioinformatics methods for analyzing sequencing data.
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Affiliation(s)
- Zhenhua Yu
- School of Information Engineering, Ningxia University, Yinchuan, 750021, China.
| | - Fang Du
- School of Information Engineering, Ningxia University, Yinchuan, 750021, China
| | - Rongjun Ban
- Hefei National Laboratory for Physical Sciences at Microscale, USTC-SJH Joint Center for Human Reproduction and Genetics, School of Life Sciences, University of Science and Technology of China, Hefei, 230027, China
| | - Yuanwei Zhang
- Hefei National Laboratory for Physical Sciences at Microscale, USTC-SJH Joint Center for Human Reproduction and Genetics, School of Life Sciences, University of Science and Technology of China, Hefei, 230027, China.
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19
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Yue JX, Liti G. simuG: a general-purpose genome simulator. Bioinformatics 2020; 35:4442-4444. [PMID: 31116378 PMCID: PMC6821417 DOI: 10.1093/bioinformatics/btz424] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Revised: 05/01/2019] [Accepted: 05/17/2019] [Indexed: 11/12/2022] Open
Abstract
SUMMARY Simulated genomes with pre-defined and random genomic variants can be very useful for benchmarking genomic and bioinformatics analyses. Here we introduce simuG, a lightweight tool for simulating the full-spectrum of genomic variants (single nucleotide polymorphisms, Insertions/Deletions, copy number variants, inversions and translocations) for any organisms (including human). The simplicity and versatility of simuG make it a unique general-purpose genome simulator for a wide-range of simulation-based applications. AVAILABILITY AND IMPLEMENTATION Code in Perl along with user manual and testing data is available at https://github.com/yjx1217/simuG. This software is free for use under the MIT license. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jia-Xing Yue
- Université Côte d'Azur, CNRS, INSERM, IRCAN, Nice 06107, France
| | - Gianni Liti
- Université Côte d'Azur, CNRS, INSERM, IRCAN, Nice 06107, France
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20
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Giguere C, Dubey HV, Sarsani VK, Saddiki H, He S, Flaherty P. SCSIM: Jointly simulating correlated single-cell and bulk next-generation DNA sequencing data. BMC Bioinformatics 2020; 21:215. [PMID: 32456609 PMCID: PMC7249349 DOI: 10.1186/s12859-020-03550-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Accepted: 05/18/2020] [Indexed: 11/21/2022] Open
Abstract
Background Recently, it has become possible to collect next-generation DNA sequencing data sets that are composed of multiple samples from multiple biological units where each of these samples may be from a single cell or bulk tissue. Yet, there does not yet exist a tool for simulating DNA sequencing data from such a nested sampling arrangement with single-cell and bulk samples so that developers of analysis methods can assess accuracy and precision. Results We have developed a tool that simulates DNA sequencing data from hierarchically grouped (correlated) samples where each sample is designated bulk or single-cell. Our tool uses a simple configuration file to define the experimental arrangement and can be integrated into software pipelines for testing of variant callers or other genomic tools. Conclusions The DNA sequencing data generated by our simulator is representative of real data and integrates seamlessly with standard downstream analysis tools.
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Affiliation(s)
- Collin Giguere
- Department of Mathematics & Statistics, University of Massachusetts Amherst, 710 N. Pleasant St., Amherst, 01003, USA
| | - Harsh Vardhan Dubey
- Department of Mathematics & Statistics, University of Massachusetts Amherst, 710 N. Pleasant St., Amherst, 01003, USA
| | - Vishal Kumar Sarsani
- Department of Mathematics & Statistics, University of Massachusetts Amherst, 710 N. Pleasant St., Amherst, 01003, USA
| | - Hachem Saddiki
- School of Public Health, University of Massachusetts Amherst, Amherst, 01003, USA
| | - Shai He
- Department of Mathematics & Statistics, University of Massachusetts Amherst, 710 N. Pleasant St., Amherst, 01003, USA
| | - Patrick Flaherty
- Department of Mathematics & Statistics, University of Massachusetts Amherst, 710 N. Pleasant St., Amherst, 01003, USA.
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21
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Yuan X, Gao M, Bai J, Duan J. SVSR: A Program to Simulate Structural Variations and Generate Sequencing Reads for Multiple Platforms. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:1082-1091. [PMID: 30334804 DOI: 10.1109/tcbb.2018.2876527] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Structural variation accounts for a major fraction of mutations in the human genome and confers susceptibility to complex diseases. Next generation sequencing along with the rapid development of computational methods provides a cost-effective procedure to detect such variations. Simulation of structural variations and sequencing reads with real characteristics is essential for benchmarking the computational methods. Here, we develop a new program, SVSR, to simulate five types of structural variations (indels, tandem duplication, CNVs, inversions, and translocations) and SNPs for the human genome and to generate sequencing reads with features from popular platforms (Illumina, SOLiD, 454, and Ion Torrent). We adopt a selection model trained from real data to predict copy number states, starting from the first site of a particular genome to the end. Furthermore, we utilize references of microbial genomes to produce insertion fragments and design probabilistic models to imitate inversions and translocations. Moreover, we create platform-specific errors and base quality profiles to generate normal, tumor, or normal-tumor mixture reads. Experimental results show that SVSR could capture more features that are realistic and generate datasets with satisfactory quality scores. SVSR is able to evaluate the performance of structural variation detection methods and guide the development of new computational methods.
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22
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Li N, Yang J, Zhu W, Liang Y. MVSC: A Multi-variation Simulator of Cancer Genome. Comb Chem High Throughput Screen 2020; 23:326-333. [PMID: 32183666 DOI: 10.2174/1386207323666200317121136] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Revised: 11/29/2019] [Accepted: 02/27/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Many forms of variations exist in the genome, which are the main causes of individual phenotypic differences. The detection of variants, especially those located in the tumor genome, still faces many challenges due to the complexity of the genome structure. Thus, the performance assessment of variation detection tools using next-generation sequencing platforms is urgently needed. METHOD We have created a software package called the Multi-Variation Simulator of Cancer genomes (MVSC) to simulate common genomic variants, including single nucleotide polymorphisms, small insertion and deletion polymorphisms, and structural variations (SVs), which are analogous to human somatically acquired variations. Three sets of variations embedded in genomic sequences in different periods were dynamically and sequentially simulated one by one. RESULTS In cancer genome simulation, complex SVs are important because this type of variation is characteristic of the tumor genome structure. Overlapping variations of different sizes can also coexist in the same genome regions, adding to the complexity of cancer genome architecture. Our results show that MVSC can efficiently simulate a variety of genomic variants that cannot be simulated by existing software packages. CONCLUSION The MVSC-simulated variants can be used to assess the performance of existing tools designed to detect SVs in next-generation sequencing data, and we also find that MVSC is memory and time-efficient compared with similar software packages.
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Affiliation(s)
- Ning Li
- School of Information and Electronic Engineering, Wuzhou University, Wuzhou, China
| | - Jialiang Yang
- Department of Mathematics and Statistics, Hainan Normal University, Haikou, Hainan 571158, China
| | - Wen Zhu
- Department of Mathematics and Statistics, Hainan Normal University, Haikou, Hainan 571158, China.,College of Computer Science and Electronic Engineering, Hunan University, Hunan, China
| | - Ying Liang
- College of Computer Science and Electronic Engineering, Hunan University, Hunan, China.,College of Computer and Information Engineering, Jiangxi Agricultural University, Nanchang 330000, China
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23
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Xing Y, Dabney AR, Li X, Wang G, Gill CA, Casola C. SECNVs: A Simulator of Copy Number Variants and Whole-Exome Sequences From Reference Genomes. Front Genet 2020; 11:82. [PMID: 32153642 PMCID: PMC7046838 DOI: 10.3389/fgene.2020.00082] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Accepted: 01/24/2020] [Indexed: 01/26/2023] Open
Abstract
Copy number variants are duplications and deletions of the genome that play an important role in phenotypic changes and human disease. Many software applications have been developed to detect copy number variants using either whole-genome sequencing or whole-exome sequencing data. However, there is poor agreement in the results from these applications. Simulated datasets containing copy number variants allow comprehensive comparisons of the operating characteristics of existing and novel copy number variant detection methods. Several software applications have been developed to simulate copy number variants and other structural variants in whole-genome sequencing data. However, none of the applications reliably simulate copy number variants in whole-exome sequencing data. We have developed and tested Simulator of Exome Copy Number Variants (SECNVs), a fast, robust and customizable software application for simulating copy number variants and whole-exome sequences from a reference genome. SECNVs is easy to install, implements a wide range of commands to customize simulations, can output multiple samples at once, and incorporates a pipeline to output rearranged genomes, short reads and BAM files in a single command. Variants generated by SECNVs are detected with high sensitivity and precision by tools commonly used to detect copy number variants. SECNVs is publicly available at https://github.com/YJulyXing/SECNVs.
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Affiliation(s)
- Yue Xing
- Interdisciplinary Program in Genetics, Texas A&M University, College Station, TX, United States
- Department of Statistics, Texas A&M University, College Station, TX, United States
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, United States
| | - Alan R. Dabney
- Department of Statistics, Texas A&M University, College Station, TX, United States
| | - Xiao Li
- Department of Molecular and Cellular Medicine, Texas A&M University, College Station, TX, United States
| | - Guosong Wang
- Department of Animal Science, Texas A&M University, College Station, TX, United States
| | - Clare A. Gill
- Department of Animal Science, Texas A&M University, College Station, TX, United States
| | - Claudio Casola
- Department of Ecosystem Science and Management, Texas A&M University, College Station, TX, United States
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24
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Bani Baker Q, Hammad M, Al-Rashdan W, Jararweh Y, AL-Smadi M, Al-Zinati M. Comprehensive comparison of cloud-based NGS data analysis and alignment tools. INFORMATICS IN MEDICINE UNLOCKED 2020. [DOI: 10.1016/j.imu.2020.100296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022] Open
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25
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Li D, Kim W, Wang L, Yoon KA, Park B, Park C, Kong SY, Hwang Y, Baek D, Lee ES, Won S. Comparison of INDEL Calling Tools with Simulation Data and Real Short-Read Data. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019; 16:1635-1644. [PMID: 30004886 DOI: 10.1109/tcbb.2018.2854793] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Insertions and deletions (INDELs) comprise a significant proportion of human genetic variation, and recent papers have revealed that many human diseases may be attributable to INDELs. With the development of next-generation sequencing (NGS) technology, many statistical/computational tools have been developed for calling INDELs. However, there are differences among those tools, and comparisons among them have been limited. In order to better understand these inter-tool differences, five popular and publicly available INDEL calling tools-GATK HaplotypeCaller, Platypus, VarScan2, Scalpel, and GotCloud-were evaluated using simulation data, 1000 Genomes Project data, and family-based sequencing data. The accuracy of INDEL calling by each tool was mainly evaluated by concordance rates. Family-based sequencing data, which consisted of 49 individuals from eight Korean families, were used to calculate Mendelian error rates. Our comparison results show that GATK HaplotypeCaller usually performs the best and that joint calling with Platypus can lead to additional improvements in accuracy. The result of this study provides important information regarding future directions for the variant detection and the algorithms development.
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26
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Yao Y, Jin Z, Lee JH. An improved statistical model for taxonomic assignment of metagenomics. BMC Genet 2018; 19:98. [PMID: 30373533 PMCID: PMC6206629 DOI: 10.1186/s12863-018-0680-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2018] [Accepted: 10/02/2018] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND With the advances in the next-generation sequencing technologies, researchers can now rapidly examine the composition of samples from humans and their surroundings. To enhance the accuracy of taxonomy assignments in metagenomic samples, we developed a method that allows multiple mismatch probabilities from different genomes. RESULTS We extended the algorithm of taxonomic assignment of metagenomic sequence reads (TAMER) by developing an improved method that can set a different mismatch probability for each genome rather than imposing a single parameter for all genomes, thereby obtaining a greater degree of accuracy. This method, which we call TADIP (Taxonomic Assignment of metagenomics based on DIfferent Probabilities), was comprehensively tested in simulated and real datasets. The results support that TADIP improved the performance of TAMER especially in large sample size datasets with high complexity. CONCLUSIONS TADIP was developed as a statistical model to improve the estimate accuracy of taxonomy assignments. Based on its varying mismatch probability setting and correlated variance matrix setting, its performance was enhanced for high complexity samples when compared with TAMER.
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Affiliation(s)
- Yujing Yao
- Department of Biostatistics, Columbia University, New York, NY, USA
| | - Zhezhen Jin
- Department of Biostatistics, Columbia University, New York, NY, USA
| | - Joseph H Lee
- Sergievsky Center, Taub Institute, and Departments of Epidemiology and Neurology, Columbia University, New York, NY, USA. .,Sergievsky Center, Columbia University, 630 West 168th Street, P&S Unit 16, New York, NY, 10032, USA.
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27
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Xia LC, Ai D, Lee H, Andor N, Li C, Zhang NR, Ji HP. SVEngine: an efficient and versatile simulator of genome structural variations with features of cancer clonal evolution. Gigascience 2018; 7:5049476. [PMID: 29982625 PMCID: PMC6057526 DOI: 10.1093/gigascience/giy081] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2018] [Revised: 05/22/2018] [Accepted: 06/26/2018] [Indexed: 11/29/2022] Open
Abstract
Background Simulating genome sequence data with variant features facilitates the development and benchmarking of structural variant analysis programs. However, there are only a few data simulators that provide structural variants in silico and even fewer that provide variants with different allelic fraction and haplotypes. Findings We developed SVEngine, an open-source tool to address this need. SVEngine simulates next-generation sequencing data with embedded structural variations. As input, SVEngine takes template haploid sequences (FASTA) and an external variant file, a variant distribution file, and/or a clonal phylogeny tree file (NEWICK) as input. Subsequently, it simulates and outputs sequence contigs (FASTAs), sequence reads (FASTQs), and/or post-alignment files (BAMs). All of the files contain the desired variants, along with BED files containing the ground truth. SVEngine's flexible design process enables one to specify size, position, and allelic fraction for deletions, insertions, duplications, inversions, and translocations. Finally, SVEngine simulates sequence data that replicate the characteristics of a sequencing library with mixed sizes of DNA insert molecules. To improve the compute speed, SVEngine is highly parallelized to reduce the simulation time. Conclusions We demonstrated the versatile features of SVEngine and its improved runtime comparisons with other available simulators. SVEngine's features include the simulation of locus-specific variant frequency designed to mimic the phylogeny of cancer clonal evolution. We validated SVEngine's accuracy by simulating genome-wide structural variants of NA12878 and a heterogeneous cancer genome. Our evaluation included checking various sequencing mapping features such as coverage change, read clipping, insert size shift, and neighboring hanging read pairs for representative variant types. Structural variant callers Lumpy and Manta and tumor heterogeneity estimator THetA2 were able to perform realistically on the simulated data. SVEngine is implemented as a standard Python package and is freely available for academic use .
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Affiliation(s)
- Li Charlie Xia
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, 269 Campus Drive, Stanford, CA 94305
- Department of Statistics, the Wharton School, University of Pennsylvania, 3730 Walnut Street, Philadelphia, PA 18014
| | - Dongmei Ai
- School of Mathematics and Physics, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing 100083 P. R. China
| | - Hojoon Lee
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, 269 Campus Drive, Stanford, CA 94305
| | - Noemi Andor
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, 269 Campus Drive, Stanford, CA 94305
| | - Chao Li
- School of Mathematics and Physics, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing 100083 P. R. China
| | - Nancy R Zhang
- Department of Statistics, the Wharton School, University of Pennsylvania, 3730 Walnut Street, Philadelphia, PA 18014
| | - Hanlee P Ji
- Division of Oncology, Department of Medicine, Stanford University School of Medicine, 269 Campus Drive, Stanford, CA 94305
- Stanford Genome Technology Center, Stanford University, 3165 Porter Drive, Palo Alto, CA 94304
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28
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Boenn M. ShRangeSim: Simulation of Single Nucleotide Polymorphism Clusters in Next-Generation Sequencing Data. J Comput Biol 2018; 25:613-622. [PMID: 29658778 DOI: 10.1089/cmb.2018.0007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Genomic variations are in the focus of research to uncover mechanisms of host-pathogen interactions and diseases such as cancer. Nowadays, next-generation sequencing (NGS) data are analyzed through dedicated pipelines to detect them. Surrogate NGS data in conjunction with genomic variations help to evaluate pipelines and validate their outcomes, fostering selection of proper tools for a given scientific question. I describe how existing approaches for simulating NGS data in conjunction with genomic variations fail to model local enrichments of single nucleotide polymorphisms (SNPs), so called SNP clusters. Two distributions for count data are applied to publicly available collections of genomic variations. The results suggest modeling of SNP cluster sizes by overdispersion-aware distributions.
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Affiliation(s)
- Markus Boenn
- 1 Institute of Computer Science, Martin Luther University Halle-Wittenberg , Halle/Saale, Germany .,2 Department of Soil Ecology, UFZ - Helmholtz Centre for Environmental Research , Halle/Saale, Germany .,3 German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig , Leipzig, Germany
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Semeraro R, Orlandini V, Magi A. Xome-Blender: A novel cancer genome simulator. PLoS One 2018; 13:e0194472. [PMID: 29621252 PMCID: PMC5886411 DOI: 10.1371/journal.pone.0194472] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Accepted: 02/05/2018] [Indexed: 11/18/2022] Open
Abstract
The adoption of next generation sequencing based methods in cancer research allowed for the investigation of the complex genetic structure of tumor samples. In the last few years, considerable importance was given to the research of somatic variants and several computational approaches were developed for this purpose. Despite continuous improvements to these programs, the validation of their results it’s a hard challenge due to multiple sources of error. To overcome this drawback different simulation approaches are used to generate synthetic samples but they are often based on the addition of artificial mutations that mimic the complexity of genomic variations. For these reasons, we developed a novel software, Xome-Blender, that generates synthetic cancer genomes with user defined features such as the number of subclones, the number of somatic variants and the presence of copy number alterations (CNAs), without the addition of any synthetic element. The singularity of our method is the “morphological approach” used to generate mutation events. To demonstrate the power of our tool we used it to address the hard challenge of evaluating the performance of nine state-of-the-art somatic variant calling methods for small and large variants (VarScan2, MuTect, Shimmer, BCFtools, Strelka, EXCAVATOR2, Control-FREEC and CopywriteR). Through these analyses we observed that by using Xome-Blender data it is possible to appraise small differences between their performance and we have designated VarScan2 and EXCAVATOR2 as best tool for this kind of applications. Xome-Blender is unix-based, licensed under the GPLv3 and freely available at https://github.com/rsemeraro/XomeBlender.
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Affiliation(s)
- Roberto Semeraro
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
- * E-mail:
| | - Valerio Orlandini
- Medical Genetics Unit, Meyer Children’s University Hospital, Florence, Italy
| | - Alberto Magi
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
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Zhao M, Liu D, Qu H. Systematic review of next-generation sequencing simulators: computational tools, features and perspectives. Brief Funct Genomics 2018; 16:121-128. [PMID: 27069250 DOI: 10.1093/bfgp/elw012] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
High-throughput next-generation sequencing (NGS) technologies have rapidly generated a large volume of genomic data. To aid the development and evaluation of new statistical models and computational methods, NGS-based simulators have been proposed to construct better experimental workflows. However, the comparative performance of these NGS simulators remains unclear. In this review, we conducted a comprehensive investigation of NGS simulators for various sequencing techniques, including DNA sequencing, metagenomic sequencing, RNA-seq, ChIP-seq and bisulfite sequencing for methylation.
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Xia Y, Liu Y, Deng M, Xi R. Pysim-sv: a package for simulating structural variation data with GC-biases. BMC Bioinformatics 2017; 18:53. [PMID: 28361688 PMCID: PMC5374556 DOI: 10.1186/s12859-017-1464-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Background Structural variations (SVs) are wide-spread in human genomes and may have important implications in disease-related and evolutionary studies. High-throughput sequencing (HTS) has become a major platform for SV detection and simulation serves as a powerful and cost-effective approach for benchmarking SV detection algorithms. Accurate performance assessment by simulation requires the simulator capable of generating simulation data with all important features of real data, such GC biases in HTS data and various complexities in tumor data. However, no available package has systematically addressed all issues in data simulation for SV benchmarking. Results Pysim-sv is a package for simulating HTS data to evaluate performance of SV detection algorithms. Pysim-sv can introduce a wide spectrum of germline and somatic genomic variations. The package contains functionalities to simulate tumor data with aneuploidy and heterogeneous subclones, which is very useful in assessing algorithm performance in tumor studies. Furthermore, Pysim-sv can introduce GC-bias, the most important and prevalent bias in HTS data, in the simulated HTS data. Conclusions Pysim-sv provides an unbiased toolkit for evaluating HTS-based SV detection algorithms.
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Affiliation(s)
- Yuchao Xia
- School of Mathematics Science and Center for Statistical Science, Peking University, Yiheyuan Road 5, Beijing, 100871, China
| | - Yun Liu
- School of Mathematics Science and Center for Statistical Science, Peking University, Yiheyuan Road 5, Beijing, 100871, China
| | - Minghua Deng
- School of Mathematics Science and Center for Statistical Science, Peking University, Yiheyuan Road 5, Beijing, 100871, China.
| | - Ruibin Xi
- School of Mathematics Science and Center for Statistical Science, Peking University, Yiheyuan Road 5, Beijing, 100871, China.
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Stephens ZD, Hudson ME, Mainzer LS, Taschuk M, Weber MR, Iyer RK. Simulating Next-Generation Sequencing Datasets from Empirical Mutation and Sequencing Models. PLoS One 2016; 11:e0167047. [PMID: 27893777 PMCID: PMC5125660 DOI: 10.1371/journal.pone.0167047] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2016] [Accepted: 11/08/2016] [Indexed: 12/31/2022] Open
Abstract
An obstacle to validating and benchmarking methods for genome analysis is that there are few reference datasets available for which the “ground truth” about the mutational landscape of the sample genome is known and fully validated. Additionally, the free and public availability of real human genome datasets is incompatible with the preservation of donor privacy. In order to better analyze and understand genomic data, we need test datasets that model all variants, reflecting known biology as well as sequencing artifacts. Read simulators can fulfill this requirement, but are often criticized for limited resemblance to true data and overall inflexibility. We present NEAT (NExt-generation sequencing Analysis Toolkit), a set of tools that not only includes an easy-to-use read simulator, but also scripts to facilitate variant comparison and tool evaluation. NEAT has a wide variety of tunable parameters which can be set manually on the default model or parameterized using real datasets. The software is freely available at github.com/zstephens/neat-genreads.
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Affiliation(s)
- Zachary D. Stephens
- Department of Electrical and Computer Engineering, Univ. of Illinois at Urbana-Champaign, Urbana, IL, United States of America
- * E-mail:
| | - Matthew E. Hudson
- Department of Crop Sciences, Univ. of Illinois at Urbana-Champaign, Urbana, IL, United States of America
- Institute for Genomic Biology, Univ. of Illinois at Urbana-Champaign, Urbana, IL, United States of America
| | - Liudmila S. Mainzer
- Institute for Genomic Biology, Univ. of Illinois at Urbana-Champaign, Urbana, IL, United States of America
- National Center for Supercomputing Applications, Univ. of Illinois at Urbana-Champaign, Urbana, IL, United States of America
| | - Morgan Taschuk
- Ontario Institute for Cancer Research, Toronto, ON, Canada
| | - Matthew R. Weber
- National Center for Supercomputing Applications, Univ. of Illinois at Urbana-Champaign, Urbana, IL, United States of America
| | - Ravishankar K. Iyer
- Department of Electrical and Computer Engineering, Univ. of Illinois at Urbana-Champaign, Urbana, IL, United States of America
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Zheng Q, Grice EA. AlignerBoost: A Generalized Software Toolkit for Boosting Next-Gen Sequencing Mapping Accuracy Using a Bayesian-Based Mapping Quality Framework. PLoS Comput Biol 2016; 12:e1005096. [PMID: 27706155 PMCID: PMC5051939 DOI: 10.1371/journal.pcbi.1005096] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2016] [Accepted: 08/02/2016] [Indexed: 01/09/2023] Open
Abstract
Accurate mapping of next-generation sequencing (NGS) reads to reference genomes is crucial for almost all NGS applications and downstream analyses. Various repetitive elements in human and other higher eukaryotic genomes contribute in large part to ambiguously (non-uniquely) mapped reads. Most available NGS aligners attempt to address this by either removing all non-uniquely mapping reads, or reporting one random or "best" hit based on simple heuristics. Accurate estimation of the mapping quality of NGS reads is therefore critical albeit completely lacking at present. Here we developed a generalized software toolkit "AlignerBoost", which utilizes a Bayesian-based framework to accurately estimate mapping quality of ambiguously mapped NGS reads. We tested AlignerBoost with both simulated and real DNA-seq and RNA-seq datasets at various thresholds. In most cases, but especially for reads falling within repetitive regions, AlignerBoost dramatically increases the mapping precision of modern NGS aligners without significantly compromising the sensitivity even without mapping quality filters. When using higher mapping quality cutoffs, AlignerBoost achieves a much lower false mapping rate while exhibiting comparable or higher sensitivity compared to the aligner default modes, therefore significantly boosting the detection power of NGS aligners even using extreme thresholds. AlignerBoost is also SNP-aware, and higher quality alignments can be achieved if provided with known SNPs. AlignerBoost’s algorithm is computationally efficient, and can process one million alignments within 30 seconds on a typical desktop computer. AlignerBoost is implemented as a uniform Java application and is freely available at https://github.com/Grice-Lab/AlignerBoost.
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Affiliation(s)
- Qi Zheng
- Department of Dermatology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- * E-mail: (QZ); (EAG)
| | - Elizabeth A. Grice
- Department of Dermatology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- * E-mail: (QZ); (EAG)
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Escalona M, Rocha S, Posada D. A comparison of tools for the simulation of genomic next-generation sequencing data. Nat Rev Genet 2016; 17:459-69. [PMID: 27320129 PMCID: PMC5224698 DOI: 10.1038/nrg.2016.57] [Citation(s) in RCA: 102] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Computer simulation of genomic data has become increasingly popular for assessing and validating biological models or for gaining an understanding of specific data sets. Several computational tools for the simulation of next-generation sequencing (NGS) data have been developed in recent years, which could be used to compare existing and new NGS analytical pipelines. Here we review 23 of these tools, highlighting their distinct functionality, requirements and potential applications. We also provide a decision tree for the informed selection of an appropriate NGS simulation tool for the specific question at hand.
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Affiliation(s)
- Merly Escalona
- Department of Biochemistry, Genetics and Immunology, University of Vigo, Vigo 36310, Spain
| | - Sara Rocha
- Department of Biochemistry, Genetics and Immunology, University of Vigo, Vigo 36310, Spain
| | - David Posada
- Department of Biochemistry, Genetics and Immunology, University of Vigo, Vigo 36310, Spain
- Institute of Biomedical Research of Vigo (IBIV), University of Vigo, Vigo 36310, Spain
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Krishnan NM, Jain P, Gupta S, Hariharan AK, Panda B. An Improved Genome Assembly of Azadirachta indica A. Juss. G3 (BETHESDA, MD.) 2016. [PMID: 27172223 DOI: 10.1534/g1533.1116.030056] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 04/14/2023]
Abstract
Neem (Azadirachta indica A. Juss.), an evergreen tree of the Meliaceae family, is known for its medicinal, cosmetic, pesticidal and insecticidal properties. We had previously sequenced and published the draft genome of a neem plant, using mainly short read sequencing data. In this report, we present an improved genome assembly generated using additional short reads from Illumina and long reads from Pacific Biosciences SMRT sequencer. We assembled short reads and error-corrected long reads using Platanus, an assembler designed to perform well for heterozygous genomes. The updated genome assembly (v2.0) yielded 3- and 3.5-fold increase in N50 and N75, respectively; 2.6-fold decrease in the total number of scaffolds; 1.25-fold increase in the number of valid transcriptome alignments; 13.4-fold less misassembly and 1.85-fold increase in the percentage repeat, over the earlier assembly (v1.0). The current assembly also maps better to the genes known to be involved in the terpenoid biosynthesis pathway. Together, the data represent an improved assembly of the A. indica genome.
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Affiliation(s)
- Neeraja M Krishnan
- Ganit Labs, Bio-IT Centre, Institute of Bioinformatics and Applied Biotechnology, Bangalore 560100, India
| | - Prachi Jain
- Ganit Labs, Bio-IT Centre, Institute of Bioinformatics and Applied Biotechnology, Bangalore 560100, India
| | - Saurabh Gupta
- Ganit Labs, Bio-IT Centre, Institute of Bioinformatics and Applied Biotechnology, Bangalore 560100, India
| | - Arun K Hariharan
- Ganit Labs, Bio-IT Centre, Institute of Bioinformatics and Applied Biotechnology, Bangalore 560100, India
| | - Binay Panda
- Ganit Labs, Bio-IT Centre, Institute of Bioinformatics and Applied Biotechnology, Bangalore 560100, India Strand Life Sciences, Bangalore 560024, India
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Abstract
Neem (Azadirachta indica A. Juss.), an evergreen tree of the Meliaceae family, is known for its medicinal, cosmetic, pesticidal and insecticidal properties. We had previously sequenced and published the draft genome of a neem plant, using mainly short read sequencing data. In this report, we present an improved genome assembly generated using additional short reads from Illumina and long reads from Pacific Biosciences SMRT sequencer. We assembled short reads and error-corrected long reads using Platanus, an assembler designed to perform well for heterozygous genomes. The updated genome assembly (v2.0) yielded 3- and 3.5-fold increase in N50 and N75, respectively; 2.6-fold decrease in the total number of scaffolds; 1.25-fold increase in the number of valid transcriptome alignments; 13.4-fold less misassembly and 1.85-fold increase in the percentage repeat, over the earlier assembly (v1.0). The current assembly also maps better to the genes known to be involved in the terpenoid biosynthesis pathway. Together, the data represent an improved assembly of the A. indica genome.
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Yuan X, Zhang J, Yang L. IntSIM: An Integrated Simulator of Next-Generation Sequencing Data. IEEE Trans Biomed Eng 2016; 64:441-451. [PMID: 27164567 DOI: 10.1109/tbme.2016.2560939] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVE Next-generation sequencing data has been widely used for DNA variant discovery and tumor study through computational tools. Effective simulation of such data with many realistic features is very necessary for testing existing tools and guiding the development of new tools. METHODS We present an integrated simulation system, IntSIM, to simulate common DNA variants and to generate sequencing reads for mixture genomes. IntSIM has three novel features in comparison with other simulation programs: 1) it is able to simulate both germline and somatic variants in the same sequence, 2) it deals with tumor purity so as to generate reads corresponding to heterogeneous genomes and also produce tumor-normal matched samples, and 3) it simulates correlations among SNPs, among CNVs/CNAs based on HMM models trained from real sequencing genomes, and can simulates broad and focal CNV/CNA events. RESULTS The simulation data of IntSIM can reflect characteristics observed from real data and are consistent with input parameters. The IntSIM software package is freely available at http://intsim.sourceforge.net/. CONCLUSION Based on a great number of experiments, IntSIM performs better than other program for some scenarios, such as simulation of heterozygous SNPs, CNVs/CNAs, and can achieve some functions that other programs cannot achieve. SIGNIFICANCE Simulation with IntSIM can be expected to evaluate performance of methods in detecting various types of variants, analyzing tumor samples, and especially providing a realistic assessment of effect of tumor purity on identification of somatic mutations.
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Hershberg U, Luning Prak ET. The analysis of clonal expansions in normal and autoimmune B cell repertoires. Philos Trans R Soc Lond B Biol Sci 2016; 370:rstb.2014.0239. [PMID: 26194753 PMCID: PMC4528416 DOI: 10.1098/rstb.2014.0239] [Citation(s) in RCA: 83] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Clones are the fundamental building blocks of immune repertoires. The number of different clones relates to the diversity of the repertoire, whereas their size and sequence diversity are linked to selective pressures. Selective pressures act both between clones and within different sequence variants of a clone. Understanding how clonal selection shapes the immune repertoire is one of the most basic questions in all of immunology. But how are individual clones defined? Here we discuss different approaches for defining clones, starting with how antibodies are diversified during different stages of B cell development. Next, we discuss how clones are defined using different experimental methods. We focus on high-throughput sequencing datasets, and the computational challenges and opportunities that these data have for mining the antibody repertoire landscape. We discuss methods that visualize sequence variants within the same clone and allow us to consider collections of shared mutations to determine which sequences share a common ancestry. Finally, we comment on features of frequently encountered expanded B cell clones that may be of particular interest in the setting of autoimmunity and other chronic conditions.
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Affiliation(s)
- Uri Hershberg
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Bossone 7-711, 3141 Chestnut Street, Philadelphia, PA 19104, USA Department of Immunology and Microbiology, College of Medicine, Drexel University, Bossone 7-711, 3141 Chestnut Street, Philadelphia, PA 19104, USA
| | - Eline T Luning Prak
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, 405B Stellar Chance Labs, 422 Curie Boulevard, Philadelphia, PA 19104, USA
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Břinda K, Boeva V, Kucherov G. RNF: a general framework to evaluate NGS read mappers. Bioinformatics 2015; 32:136-9. [PMID: 26353839 PMCID: PMC4681991 DOI: 10.1093/bioinformatics/btv524] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2015] [Accepted: 08/31/2015] [Indexed: 11/12/2022] Open
Abstract
Motivation: Read simulators combined with alignment evaluation tools provide the most straightforward way to evaluate and compare mappers. Simulation of reads is accompanied by information about their positions in the source genome. This information is then used to evaluate alignments produced by the mapper. Finally, reports containing statistics of successful read alignments are created. In default of standards for encoding read origins, every evaluation tool has to be made explicitly compatible with the simulator used to generate reads. Results: To solve this obstacle, we have created a generic format Read Naming Format (Rnf) for assigning read names with encoded information about original positions. Futhermore, we have developed an associated software package RnfTools containing two principal components. MIShmash applies one of popular read simulating tools (among DwgSim, Art, Mason, CuReSim, etc.) and transforms the generated reads into Rnf format. LAVEnder evaluates then a given read mapper using simulated reads in Rnf format. A special attention is payed to mapping qualities that serve for parametrization of Roc curves, and to evaluation of the effect of read sample contamination. Availability and implementation: RnfTools: http://karel-brinda.github.io/rnftools Spec. of Rnf: http://karel-brinda.github.io/rnf-spec Contact:karel.brinda@univ-mlv.fr
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Affiliation(s)
- Karel Břinda
- LIGM/CNRS, Université Paris-Est, 77454 Marne-la-Vallée, France
| | - Valentina Boeva
- Inserm, U900, Bioinformatics, Biostatistics, Epidemiology and Computational Systems Biology of Cancer, 75248 Paris, France, Institut Curie, Centre de Recherche, 26 rue d'Ulm, 75248 Paris, France and Mines ParisTech, 77300 Fontainebleau, France
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