scholarly journals The genomic landscape of polymorphic human nuclear mitochondrial insertions

2014 ◽  
Author(s):  
Gargi Dayama ◽  
Sarah B Emery ◽  
Jeffrey M Kidd ◽  
Ryan E Mills
Keyword(s):  
High Throughput Sequencing ◽  
Reference Genome ◽  
Current Knowledge ◽  
Genetic Material ◽  
Purifying Selection ◽  
Genomic Variation ◽  
Human Populations ◽  
Sequencing Data ◽  
Sequencing Technologies ◽  
D Loop

The transfer of mitochondrial genetic material into the nuclear genomes of eukaryotes is a well-established phenomenon. Many studies over the past decade have utilized reference genome sequences of numerous species to characterize the prevalence and contribution of nuclear mitochondrial insertions to human diseases. The recent advancement of high throughput sequencing technologies has enabled the interrogation of genomic variation at a much finer scale, and now allows for an exploration into the diversity of polymorphic nuclear mitochondrial insertions (NumtS) in human populations. We have developed an approach to discover and genotype previously undiscovered Numt insertions using whole genome, paired-end sequencing data. We have applied this method to almost a thousand individuals in twenty populations from the 1000 Genomes Project and other data sets and identified 138 novel sites of Numt insertions, extending our current knowledge of existing Numt locations in the human genome by almost 20%. Most of the newly identified NumtS were found in less than 1% of the samples we examined, suggesting that they occur infrequently in nature or have been rapidly removed by purifying selection. We find that recent Numt insertions are derived from throughout the mitochondrial genome, including the D-loop, and have integration biases consistent with previous studies on older, fixed NumtS in the reference genome. We have further determined the complete inserted sequence for a subset of these events to define their age and origin of insertion as well as their potential impact on studies of mitochondrial heteroplasmy.

scholarly journals Tools and best practices for retrotransposon analysis using high-throughput sequencing data

Mobile DNA ◽  
2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Aurélie Teissandier ◽  
Nicolas Servant ◽  
Emmanuel Barillot ◽  
Deborah Bourc’his
Keyword(s):  
Transposable Elements ◽  
Transposable Element ◽  
Molecular Mechanisms ◽  
High Throughput Sequencing ◽  
Reference Genome ◽  
Repetitive Sequences ◽  
Simulated Data ◽  
Sequencing Data ◽  
Sequencing Technologies ◽  
Human Genomes

Abstract Background Sequencing technologies give access to a precise picture of the molecular mechanisms acting upon genome regulation. One of the biggest technical challenges with sequencing data is to map millions of reads to a reference genome. This problem is exacerbated when dealing with repetitive sequences such as transposable elements that occupy half of the mammalian genome mass. Sequenced reads coming from these regions introduce ambiguities in the mapping step. Therefore, applying dedicated parameters and algorithms has to be taken into consideration when transposable elements regulation is investigated with sequencing datasets. Results Here, we used simulated reads on the mouse and human genomes to define the best parameters for aligning transposable element-derived reads on a reference genome. The efficiency of the most commonly used aligners was compared and we further evaluated how transposable element representation should be estimated using available methods. The mappability of the different transposon families in the mouse and the human genomes was calculated giving an overview into their evolution. Conclusions Based on simulated data, we provided recommendations on the alignment and the quantification steps to be performed when transposon expression or regulation is studied, and identified the limits in detecting specific young transposon families of the mouse and human genomes. These principles may help the community to adopt standard procedures and raise awareness of the difficulties encountered in the study of transposable elements.

Population-specific genome graphs improve high-throughput sequencing data analysis: A case study on the Pan-African genome

2021 ◽  
Author(s):  
H. Serhat Tetikol ◽  
Kubra Narci ◽  
Deniz Turgut ◽  
Gungor Budak ◽  
Ozem Kalay ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Information Overload ◽  
African Ancestry ◽  
Sample Selection ◽  
Variant Calling ◽  
Population Diversity ◽  
Human Populations ◽  
Sequencing Data ◽  
Bioinformatics Pipeline ◽  
Graph Augmentation

ABSTRACTGraph-based genome reference representations have seen significant development, motivated by the inadequacy of the current human genome reference for capturing the diverse genetic information from different human populations and its inability to maintain the same level of accuracy for non-European ancestries. While there have been many efforts to develop computationally efficient graph-based bioinformatics toolkits, how to curate genomic variants and subsequently construct genome graphs remains an understudied problem that inevitably determines the effectiveness of the end-to-end bioinformatics pipeline. In this study, we discuss major obstacles encountered during graph construction and propose methods for sample selection based on population diversity, graph augmentation with structural variants and resolution of graph reference ambiguity caused by information overload. Moreover, we present the case for iteratively augmenting tailored genome graphs for targeted populations and test the proposed approach on the whole-genome samples of African ancestry. Our results show that, as more representative alternatives to linear or generic graph references, population-specific graphs can achieve significantly lower read mapping errors, increased variant calling sensitivity and provide the improvements of joint variant calling without the need of computationally intensive post-processing steps.

scholarly journals Utilizing the VirIdAl Pipeline to Search for Viruses in the Metagenomic Data of Bat Samples

Viruses ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 2006
Author(s):  
Anna Y Budkina ◽  
Elena V Korneenko ◽  
Ivan A Kotov ◽  
Daniil A Kiselev ◽  
Ilya V Artyushin ◽  
...  
Keyword(s):  
Large Scale ◽  
High Throughput Sequencing ◽  
Metagenomic Data ◽  
Sequencing Data ◽  
Viral Pathogens ◽  
Genomic Databases ◽  
Bioinformatic Pipeline ◽  
Viral Genomes ◽  
Sequencing Technologies ◽  
Viral Screening

According to various estimates, only a small percentage of existing viruses have been discovered, naturally much less being represented in the genomic databases. High-throughput sequencing technologies develop rapidly, empowering large-scale screening of various biological samples for the presence of pathogen-associated nucleotide sequences, but many organisms are yet to be attributed specific loci for identification. This problem particularly impedes viral screening, due to vast heterogeneity in viral genomes. In this paper, we present a new bioinformatic pipeline, VirIdAl, for detecting and identifying viral pathogens in sequencing data. We also demonstrate the utility of the new software by applying it to viral screening of the feces of bats collected in the Moscow region, which revealed a significant variety of viruses associated with bats, insects, plants, and protozoa. The presence of alpha and beta coronavirus reads, including the MERS-like bat virus, deserves a special mention, as it once again indicates that bats are indeed reservoirs for many viral pathogens. In addition, it was shown that alignment-based methods were unable to identify the taxon for a large proportion of reads, and we additionally applied other approaches, showing that they can further reveal the presence of viral agents in sequencing data. However, the incompleteness of viral databases remains a significant problem in the studies of viral diversity, and therefore necessitates the use of combined approaches, including those based on machine learning methods.

Virus detection in high-throughput sequencing data without a reference genome of the host

2018 ◽  
Vol 66 ◽  
pp. 180-187 ◽  
Author(s):  
Jochen Kruppa ◽  
Wendy K. Jo ◽  
Erhard van der Vries ◽  
Martin Ludlow ◽  
Albert Osterhaus ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Reference Genome ◽  
Virus Detection ◽  
Sequencing Data ◽  
High Throughput Sequencing Data

scholarly journals The theory and practice of measuring broad-range recombination rate from marker selected pools

2019 ◽  
Author(s):  
Kevin H.-C. Wei ◽  
Aditya Mantha ◽  
Doris Bachtrog
Keyword(s):  
Genetic Distance ◽  
Allele Frequency ◽  
Recombination Rate ◽  
High Throughput Sequencing ◽  
Genetic Material ◽  
Cost Effective ◽  
Theory And Practice ◽  
Rate Variation ◽  
Sequencing Data ◽  
Genome Wide

ABSTRACTRecombination is the exchange of genetic material between homologous chromosomes via physical crossovers. Pioneered by T. H. Morgan and A. Sturtevant over a century ago, methods to estimate recombination rate and genetic distance require scoring large number of recombinant individuals between molecular or visible markers. While high throughput sequencing methods have allowed for genome wide crossover detection producing high resolution maps, such methods rely on large number of recombinants individually sequenced and are therefore difficult to scale. Here, we present a simple and scalable method to infer near chromosome-wide recombination rate from marker selected pools and the corresponding analytical software MarSuPial. Rather than genotyping individuals from recombinant backcrosses, we bulk sequence marker selected pools to infer the allele frequency decay around the selected locus; since the number of recombinant individuals increases proportionally to the genetic distance from the selected locus, the allele frequency across the chromosome can be used to estimate the genetic distance and recombination rate. We mathematically demonstrate the relationship between allele frequency attenuation, recombinant fraction, genetic distance, and recombination rate in marker selected pools. Based on available chromosome-wide recombination rate models of Drosophila, we simulated read counts and determined that nonlinear local regressions (LOESS) produce robust estimates despite the high noise inherent to sequencing data. To empirically validate this approach, we show that (single) marker selected pools closely recapitulate genetic distances inferred from scoring recombinants between double markers. We theoretically determine how secondary loci with viability impacts can modulate the allele frequency decay and how to account for such effects directly from the data. We generated the recombinant map of three wild derived strains which strongly correlates with previous genome-wide measurements. Interestingly, amidst extensive recombination rate variation, multiple regions of the genomes show elevated rates across all strains. Lastly, we apply this method to estimate chromosome-wide crossover interference. Altogether, we find that marker selected pools is a simple and cost effective method for broad recombination rate estimates. Although it does not identify instances of crossovers, it can generate near chromosome-wide recombination maps in as little as one or two libraries.

scholarly journals Natrix: A Snakemake-based workflow for processing, clustering, and taxonomically assigning amplicon sequencing reads

2020 ◽  
Author(s):  
Marius Welzel ◽  
Anja Lange ◽  
Dominik Heider ◽  
Michael Schwarz ◽  
Bernd Freisleben ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Workflow Management ◽  
Amplicon Sequencing ◽  
Version Control ◽  
Marker Genes ◽  
Sequencing Data ◽  
Taxonomic Assignment ◽  
Ecological Processes ◽  
Sequencing Technologies ◽  
User Friendly

AbstractSequencing of marker genes amplified from environmental samples, known as amplicon sequencing, allows us to resolve some of the hidden diversity and elucidate evolutionary relationships and ecological processes among complex microbial communities. The analysis of large numbers of samples at high sequencing depths generated by high throughput sequencing technologies requires effcient, flexible, and reproducible bioinformatics pipelines. Only a few existing workflows can be run in a user-friendly, scalable, and reproducible manner on different computing devices using an effcient workflow management system. We present Natrix, an open-source bioinformatics workflow for preprocessing raw amplicon sequencing data. The workflow contains all analysis steps from quality assessment, read assembly, dereplication, chimera detection, split-sample merging, sequence representative assignment (OTUs or ASVs) to the taxonomic assignment of sequence representatives. The workflow is written using Snakemake, a workflow management engine for developing data analysis workflows. In addition, Conda is used for version control. Thus, Snakemake ensures reproducibility and Conda offers version control of the utilized programs. The encapsulation of rules and their dependencies support hassle-free sharing of rules between workflows and easy adaptation and extension of existing workflows. Natrix is freely available on GitHub (https://github.com/MW55/Natrix).

scholarly journals PhageTerm: a Fast and User-friendly Software to Determine Bacteriophage Termini and Packaging Mode using randomly fragmented NGS data

2017 ◽  
Author(s):  
Julian Garneau ◽  
Florence Depardieu ◽  
Louis-Charles Fortier ◽  
David Bikard ◽  
Marc Monot
Keyword(s):  
High Throughput Sequencing ◽  
Next Generation Sequencing Data ◽  
Sequencing Data ◽  
Link Type ◽  
Sequencing Technologies ◽  
Statistical Framework ◽  
Fastq Format ◽  
Viral Particles ◽  
User Friendly ◽  
Ngs Data

ABSTRACTBacteriophages are the most abundant viruses on earth and display an impressive genetic as well as morphologic diversity. Among those, the most common order of phages is the Caudovirales, whose viral particles packages linear double stranded DNA (dsDNA). In this study we investigated how the information gathered by high throughput sequencing technologies can be used to determine the DNA termini and packaging mechanisms of dsDNA phages. The wet-lab procedures traditionally used for this purpose rely on the identification and cloning of restriction fragment which can be delicate and cumbersome. Here, we developed a theoretical and statistical framework to analyze DNA termini and phage packaging mechanisms using next-generation sequencing data. Our methods, implemented in the PhageTerm software, work with sequencing reads in fastq format and the corresponding assembled phage genome.PhageTerm was validated on a set of phages with well-established packaging mechanisms representative of the termini diversity: 5’cos (lambda), 3’cos (HK97), pac (P1), headful without a pac site (T4), DTR (T7) and host fragment (Mu). In addition, we determined the termini of 9Clostridium difficilephages and 6 phages whose sequences where retrieved from the sequence read archive (SRA).A direct graphical interface is available as a Galaxy wrapper version athttps://galaxy.pasteur.frand a standalone version is accessible athttps://sourceforge.net/projects/phageterm/.

scholarly journals VPsero: Rapid Serotyping of Vibrio parahaemolyticus Using Serogroup-Specific Genes Based on Whole-Genome Sequencing Data

2021 ◽  
Vol 12 ◽  
Author(s):  
Shengzhe Bian ◽  
Yangyang Jia ◽  
Qiuyao Zhan ◽  
Nai-Kei Wong ◽  
Qinghua Hu ◽  
...  
Keyword(s):  
Vibrio Parahaemolyticus ◽  
High Throughput Sequencing ◽  
Capsular Polysaccharide ◽  
High Specificity ◽  
Gene Clusters ◽  
Whole Genome Sequencing Data ◽  
Sequencing Data ◽  
Sequencing Technologies ◽  
Marine Habitats ◽  
Specificity And Sensitivity

Vibrio parahaemolyticus has emerged as a significant enteropathogen in human and marine habitats worldwide, notably in regions where aquaculture products constitute a major nutritional source. It is a growing cause of diseases including gastroenteritis, wound infections, and septicemia. Serotyping assays use commercially available antisera to identify V. parahaemolyticus strains, but this approach is limited by high costs, complicated procedures, cross-immunoreactivity, and often subjective interpretation. By leveraging high-throughput sequencing technologies, we developed an in silico method based on comparison of gene clusters for lipopolysaccharide (LPSgc) and capsular polysaccharide (CPSgc) by firstly using the unique-gene strategy. The algorithm, VPsero, which exploits serogroup-specific genes as markers, covers 43 K and all 12 O serogroups in serotyping assays. VPsero is capable of predicting serotypes from assembled draft genomes, outputting LPSgc/CPSgc sequences, and recognizing possible novel serogroups or populations. Our tool displays high specificity and sensitivity in prediction toward V. parahaemolyticus strains, with an average sensitivity in serogroup prediction of 0.910 for O and 0.961 for K serogroups and a corresponding average specificity of 0.990 for O and 0.998 for K serogroups.

scholarly journals ChiTaH: a fast and accurate tool for identifying known human chimeric sequences from high-throughput sequencing data

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Rajesh Detroja ◽  
Alessandro Gorohovski ◽  
Olawumi Giwa ◽  
Gideon Baum ◽  
Milana Frenkel-Morgenstern
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Complex Disease ◽  
Single Cells ◽  
Reference Database ◽  
Sequencing Data ◽  
Sequencing Technologies ◽  
High Throughput Sequencing Data ◽  
Chimeric Rnas ◽  
Sensitivity Specificity

Abstract Fusion genes or chimeras typically comprise sequences from two different genes. The chimeric RNAs of such joined sequences often serve as cancer drivers. Identifying such driver fusions in a given cancer or complex disease is important for diagnosis and treatment. The advent of next-generation sequencing technologies, such as DNA-Seq or RNA-Seq, together with the development of suitable computational tools, has made the global identification of chimeras in tumors possible. However, the testing of over 20 computational methods showed these to be limited in terms of chimera prediction sensitivity, specificity, and accurate quantification of junction reads. These shortcomings motivated us to develop the first ‘reference-based’ approach termed ChiTaH (Chimeric Transcripts from High–throughput sequencing data). ChiTaH uses 43,466 non–redundant known human chimeras as a reference database to map sequencing reads and to accurately identify chimeric reads. We benchmarked ChiTaH and four other methods to identify human chimeras, leveraging both simulated and real sequencing datasets. ChiTaH was found to be the most accurate and fastest method for identifying known human chimeras from simulated and sequencing datasets. Moreover, especially ChiTaH uncovered heterogeneity of the BCR-ABL1 chimera in both bulk and single-cells of the K-562 cell line, which was confirmed experimentally.

scholarly journals Detecting gene subnetworks under selection in biological pathways

2017 ◽  
Author(s):  
Alexandre Gouy ◽  
Joséphine T. Daub ◽  
Laurent Excoffier
Keyword(s):  
Complex Traits ◽  
Gene Networks ◽  
High Throughput Sequencing ◽  
Biological Pathways ◽  
General Idea ◽  
Human Populations ◽  
Sequencing Technologies ◽  
Genetic Components ◽  
Evolutionary Features ◽  
Gene Network Analysis

ABSTRACTAdvances in high throughput sequencing technologies have created a gap between data production and functional data analysis. Indeed, phenotypes result from interactions between numerous genes, but traditional methods treat loci independently, missing important knowledge brought by network-level emerging properties. Therefore, evidencing selection acting on multiple genes affecting the evolution of complex traits remains challenging. In this context, gene network analysis provides a powerful framework to study the evolution of adaptive traits and facilitates the interpretation of genome-wide data. To tackle this problem, we developed a method to analyse gene networks that is suitable to evidence polygenic selection. The general idea is to search biological pathways for subnetworks of genes that directly interact with each other and that present unusual evolutionary features. Subnetwork search is a typical combinatorial optimization problem that we solve using a simulated annealing approach. We have applied our methodology to find signals of adaptation to high-altitude in human populations. We show that this adaptation has a clear polygenic basis and is influenced by many genetic components. Our approach improves on classical tests for selection based on single genes by identifying both new candidate genes and new biological processes involved in adaptation to altitude.

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