scholarly journals simuG: a general-purpose genome simulator

2019 ◽  
Vol 35 (21) ◽  
pp. 4442-4444 ◽  
Author(s):  
Jia-Xing Yue ◽  
Gianni Liti
Keyword(s):  
Copy Number Variants ◽  
General Purpose ◽  
Supplementary Information ◽  
Nucleotide Polymorphisms ◽  
Bioinformatics Analyses ◽  
Full Spectrum ◽  
Single Nucleotide ◽  
Genomic Variants ◽  
Wide Range ◽  
Simulation Based

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.

scholarly journals simuG: a general-purpose genome simulator

2018 ◽  
Author(s):  
Jia-Xing Yue ◽  
Gianni Liti
Keyword(s):  
General Purpose ◽  
Bioinformatics Analyses ◽  
Full Spectrum ◽  
Genomic Variants ◽  
Link Type ◽  
Wide Range ◽  
Simulation Based ◽  
Testing Data

AbstractSummarySimulated genomes with pre-defined and random genomic variants can be very useful for benchmarking genomic and bioinformatics analyses. Here we introduce simuG, a light-weighted tool for simulating the full-spectrum of genomic variants. The simplicity and versatility of simuG makes it a unique general purpose genome simulator for a wide-range of simulation-based applications.Availability and implementationCode 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.

SRG extractor: a skinny reference genome approach for reduced-representation sequencing

2019 ◽  
Vol 35 (17) ◽  
pp. 3160-3162
Author(s):  
Davoud Torkamaneh ◽  
Jérôme Laroche ◽  
Istvan Rajcan ◽  
François Belzile
Keyword(s):  
Reference Genome ◽  
Computing Time ◽  
Supplementary Information ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Scanning Method ◽  
Reduced Representation ◽  
A Genome ◽  
Wide Range ◽  
Reduced Representation Sequencing

Abstract Motivation Reduced-representation sequencing is a genome-wide scanning method for simultaneous discovery and genotyping of thousands to millions of single nucleotide polymorphisms that is used across a wide range of species. However, in this method a reproducible but very small fraction of the genome is captured for sequencing, while the resulting reads are typically aligned against the entire reference genome. Results Here we present a skinny reference genome approach in which a simplified reference genome is used to decrease computing time for data processing and to increase single nucleotide polymorphism counts and accuracy. A skinny reference genome can be integrated into any reduced-representation sequencing analytical pipeline. Availability and implementation https://bitbucket.org/jerlar73/SRG-Extractor. Supplementary information Supplementary data are available at Bioinformatics online.

Dementia key gene identification with multi-layered SNP-gene-disease network

2020 ◽  
Vol 36 (Supplement_2) ◽  
pp. i831-i839
Author(s):  
Dong-gi Lee ◽  
Myungjun Kim ◽  
Sang Joon Son ◽  
Chang Hyung Hong ◽  
Hyunjung Shin
Keyword(s):  
Candidate Genes ◽  
Learning Algorithm ◽  
Search Space ◽  
Supplementary Information ◽  
Gene Identification ◽  
Nucleotide Polymorphisms ◽  
Disease Network ◽  
Single Nucleotide ◽  
Key Genes ◽  
Significant Attention

Abstract Motivation Recently, various approaches for diagnosing and treating dementia have received significant attention, especially in identifying key genes that are crucial for dementia. If the mutations of such key genes could be tracked, it would be possible to predict the time of onset of dementia and significantly aid in developing drugs to treat dementia. However, gene finding involves tremendous cost, time and effort. To alleviate these problems, research on utilizing computational biology to decrease the search space of candidate genes is actively conducted. In this study, we propose a framework in which diseases, genes and single-nucleotide polymorphisms are represented by a layered network, and key genes are predicted by a machine learning algorithm. The algorithm utilizes a network-based semi-supervised learning model that can be applied to layered data structures. Results The proposed method was applied to a dataset extracted from public databases related to diseases and genes with data collected from 186 patients. A portion of key genes obtained using the proposed method was verified in silico through PubMed literature, and the remaining genes were left as possible candidate genes. Availability and implementation The code for the framework will be available at http://www.alphaminers.net/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals TADA – a Machine Learning Tool for Functional Annotation based Prioritisation of Putative Pathogenic CNVs

2020 ◽  
Author(s):  
J. Hertzberg ◽  
S. Mundlos ◽  
M. Vingron ◽  
G. Gallone
Keyword(s):  
Machine Learning ◽  
Functional Annotation ◽  
Copy Number Variants ◽  
Enrichment Analysis ◽  
Computational Prediction ◽  
Nucleotide Polymorphisms ◽  
Automated Classification ◽  
Single Nucleotide ◽  
Pathogenic Cnvs ◽  
Disease Impact

AbstractThe computational prediction of disease-associated genetic variation is of fundamental importance for the genomics, genetics and clinical research communities. Whereas the mechanisms and disease impact underlying coding single nucleotide polymorphisms (SNPs) and small Insertions/Deletions (InDels) have been the focus of intense study, little is known about the corresponding impact of structural variants (SVs), which are challenging to detect, phase and interpret. Few methods have been developed to prioritise larger chromosomal alterations such as Copy Number Variants (CNVs) based on their pathogenicity. We address this issue with TADA, a method to prioritise pathogenic CNVs through manual filtering and automated classification, based on an extensive catalogue of functional annotation supported by rigorous enrichment analysis. We demonstrate that our machine-learning classifiers for deletions and duplications are able to accurately predict pathogenic CNVs (AUC: 0.8042 and 0.7869, respectively) and produce a well-calibrated pathogenicity score. The combination of enrichment analysis and classifications suggests that prioritisation of pathogenic CNVs based on functional annotation is a promising approach to support clinical diagnostic and to further the understanding of mechanisms that control the disease impact of larger genomic alterations.

Genetics of Schizophrenia and Bipolar Disorder

2017 ◽  
Author(s):  
Alexander Charney ◽  
Pamela Sklar
Keyword(s):  
Bipolar Disorder ◽  
Copy Number ◽  
Psychotic Disorders ◽  
Copy Number Variants ◽  
Nucleotide Polymorphisms ◽  
Common Variants ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Number Variation

Schizophrenia and bipolar disorder are the classic psychotic disorders. Both diseases are strongly familial, but have proven recalcitrant to genetic methodologies for identifying the etiology until recently. There is now convincing genetic evidence that indicates a contribution of many DNA changes to the risk of becoming ill. For schizophrenia, there are large contributions of rare copy number variants and common single nucleotide variants, with an overall highly polygenic genetic architecture. For bipolar disorder, the role of copy number variation appears to be much less pronounced. Specific common single nucleotide polymorphisms are associated, and there is evidence for polygenicity. Several surprises have emerged from the genetic data that indicate there is significantly more molecular overlap in copy number variants between autism and schizophrenia, and in common variants between schizophrenia and bipolar disorder.

scholarly journals LongAGE: defining breakpoints of genomic structural variants through optimal and memory efficient alignments of long reads

2020 ◽  
Author(s):  
Quang Tran ◽  
Alexej Abyzov
Keyword(s):  
Copy Number Variants ◽  
Supplementary Information ◽  
Segmental Duplications ◽  
Structural Variations ◽  
Single Nucleotide ◽  
Long Reads ◽  
Nucleotide Resolution ◽  
Single Nucleotide Resolution ◽  
Genomic Structural Variants ◽  
Memory Efficient

Abstract Summary Defining the precise location of structural variations (SVs) at single-nucleotide breakpoint resolution is a challenging problem due to large gaps in alignment. Previously, Alignment with Gap Excision (AGE) enabled us to define breakpoints of SVs at single-nucleotide resolution; however, AGE requires a vast amount of memory when aligning a pair of long sequences. To address this, we developed a memory-efficient implementation—LongAGE—based on the classical Hirschberg algorithm. We demonstrate an application of LongAGE for resolving breakpoints of SVs embedded into segmental duplications on Pacific Biosciences (PacBio) reads that can be longer than 10 kb. Furthermore, we observed different breakpoints for a deletion and a duplication in the same locus, providing direct evidence that such multi-allelic copy number variants (mCNVs) arise from two or more independent ancestral mutations. Availability and implementation LongAGE is implemented in C++ and available on Github at https://github.com/Coaxecva/LongAGE. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals 3DBIONOTES v3.0: crossing molecular and structural biology data with genomic variations

2019 ◽  
Vol 35 (18) ◽  
pp. 3512-3513 ◽  
Author(s):  
Joan Segura ◽  
Ruben Sanchez-Garcia ◽  
C O S Sorzano ◽  
J M Carazo
Keyword(s):  
Gene Annotation ◽  
Supplementary Information ◽  
Fisher Exact Test ◽  
Nucleotide Polymorphisms ◽  
Contingency Analysis ◽  
Exact Test ◽  
Genomic Variants ◽  
Protein Protein Interaction ◽  
Processing Centre ◽  
Critical Regions

Abstract Motivation Many diseases are associated to single nucleotide polymorphisms that affect critical regions of proteins as binding sites or post translational modifications. Therefore, analysing genomic variants with structural and molecular biology data is a powerful framework in order to elucidate the potential causes of such diseases. Results A new version of our web framework 3DBIONOTES is presented. This version offers new tools to analyse and visualize protein annotations and genomic variants, including a contingency analysis of variants and amino acid features by means of a Fisher exact test, the integration of a gene annotation viewer to highlight protein features on gene sequences and a protein–protein interaction viewer to display protein annotations at network level. Availability and implementation The web server is available at https://3dbionotes.cnb.csic.es Supplementary information Supplementary data are available at Bioinformatics online. Contact Spanish National Institute for Bioinformatics (INB ELIXIR-ES) and Biocomputing Unit, National Centre of Biotechnology (CSIC)/Instruct Image Processing Centre, C/ Darwin nº 3, Campus of Cantoblanco, 28049 Madrid, Spain.

scholarly journals Predicting the effects of SNPs on transcription factor binding affinity

2019 ◽  
Author(s):  
Sierra S Nishizaki ◽  
Natalie Ng ◽  
Shengcheng Dong ◽  
Robert S Porter ◽  
Cody Morterud ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Binding Affinity ◽  
Association Studies ◽  
Transcription Factor Binding ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Factor Binding ◽  
Genome Wide

Abstract Motivation Genome-wide association studies have revealed that 88% of disease-associated single-nucleotide polymorphisms (SNPs) reside in noncoding regions. However, noncoding SNPs remain understudied, partly because they are challenging to prioritize for experimental validation. To address this deficiency, we developed the SNP effect matrix pipeline (SEMpl). Results SEMpl estimates transcription factor-binding affinity by observing differences in chromatin immunoprecipitation followed by deep sequencing signal intensity for SNPs within functional transcription factor-binding sites (TFBSs) genome-wide. By cataloging the effects of every possible mutation within the TFBS motif, SEMpl can predict the consequences of SNPs to transcription factor binding. This knowledge can be used to identify potential disease-causing regulatory loci. Availability and implementation SEMpl is available from https://github.com/Boyle-Lab/SEM_CPP. Supplementary information Supplementary data are available at Bioinformatics online.

PSXIV-25 Search for the association of SNP in GRM8 gene with sperm quality in stallions

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 257-257
Author(s):  
Elena Nikitkina ◽  
Anna Krutikova ◽  
Artem Musidray
Keyword(s):  
Volume Concentration ◽  
Sperm Quality ◽  
Nucleotide Polymorphisms ◽  
Nucleotide Substitutions ◽  
Single Nucleotide ◽  
Progressive Motility ◽  
Wide Range ◽  
Sperm Volume ◽  
Polymorphic Variants ◽  
Genetic Analyzer

Abstract The formation and functioning of the animal reproductive system occurs as a result of the coordinated interaction of a wide range of genes. To search for causal mutations, work was carried out to search for polymorphic variants in the marker regions detected using GWAS. The four single-nucleotide substitutions in the exon of the GRM8 gene identified during the studies and the association of these SNPs with sperm quality was carried out. Semen from 22 stallions was collected. Sperm volume, concentration and progressive motility were assessed. Sequencing of the sections of the candidate GRM8 gene was carried out using an Applied Biosystems 3500 genetic analyzer. For the rs1138419111 genotype, no significant differences were found in the studied parameters. According to the identified single nucleotide substitution rs1147388106, the highest ejaculate volume was in stallions with the GG genotype (55.9±26.5 ml) compared to stallions with the GA genotypes (32.5±13.9 ml) and AA (18.0±33,6) (p < 0.05). When analyzing data on SNP rs395286150, stallions with a heterozygous CT genotype had the best sperm quality. Thus, the cell concentration was 317.0±66.5 million/ml in stallions with the CT genotype, 209.6±58.2 and 189.5±74.9 % with the CC and TT genotypes, respectively (P < 0.05). The progressive sperm motility of stallions with the CT genotype was 65.5±20.5% versus 48.7±22.0% in stallions with the TT genotype and 48.4±18.6% with CC. Analysis of data on SNP rs394524550 revealed a significant effect of the genotype on progressive motility. Stallions with the AG genotype had a progressive motility of 64.6±16.3%, and those with GG and AA 32.7±15.7 and 49.6±18.1%, respectively (P < 0.05). Thus, as a result four single nucleotide polymorphisms were identified in the exon of the GRM gene. Three of them were significantly associated with such indicators of sperm quality as ejaculate volume, concentration and progressive motility. Project No. 0445-2021-0011.

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