scholarly journals qc3C: Reference-free quality control for Hi-C sequencing data

2021 ◽  
Vol 17 (10) ◽  
pp. e1008839
Author(s):  
Matthew Z. DeMaere ◽  
Aaron E. Darling
Keyword(s):  
Quality Indicators ◽  
High Throughput Sequencing ◽  
Software Tool ◽  
Model Organisms ◽  
Sequencing Data ◽  
Proximity Ligation ◽  
Genome Wide ◽  
Sequencing Quality ◽  
Wet Lab ◽  
Genome Assemblies

Hi-C is a sample preparation method that enables high-throughput sequencing to capture genome-wide spatial interactions between DNA molecules. The technique has been successfully applied to solve challenging problems such as 3D structural analysis of chromatin, scaffolding of large genome assemblies and more recently the accurate resolution of metagenome-assembled genomes (MAGs). Despite continued refinements, however, preparing a Hi-C library remains a complex laboratory protocol. To avoid costly failures and maximise the odds of successful outcomes, diligent quality management is recommended. Current wet-lab methods provide only a crude assay of Hi-C library quality, while key post-sequencing quality indicators used have—thus far—relied upon reference-based read-mapping. When a reference is accessible, this reliance introduces a concern for quality, where an incomplete or inexact reference skews the resulting quality indicators. We propose a new, reference-free approach that infers the total fraction of read-pairs that are a product of proximity ligation. This quantification of Hi-C library quality requires only a modest amount of sequencing data and is independent of other application-specific criteria. The algorithm builds upon the observation that proximity ligation events are likely to create k-mers that would not naturally occur in the sample. Our software tool (qc3C) is to our knowledge the first to implement a reference-free Hi-C QC tool, and also provides reference-based QC, enabling Hi-C to be more easily applied to non-model organisms and environmental samples. We characterise the accuracy of the new algorithm on simulated and real datasets and compare it to reference-based methods.

scholarly journals qc3C: reference-free quality control for Hi-C sequencing data

2021 ◽  
Author(s):  
Matthew Z. DeMaere ◽  
Aaron E. Darling
Keyword(s):  
High Throughput Sequencing ◽  
Software Tool ◽  
Model Organisms ◽  
Sequencing Data ◽  
Proximity Ligation ◽  
Genome Wide ◽  
Costly Failure ◽  
Wet Lab ◽  
Genome Assemblies

AbstractHi-C is a sample preparation method that enables high-throughput sequencing to capture genome-wide spatial interactions between DNA molecules. The technique has been successfully applied to solve challenging problems such as 3D structural analysis of chromatin, scaffolding of large genome assemblies and more recently the accurate resolution of metagenome-assembled genomes (MAGs). Despite continued refinements, however, Hi-C library preparation remains a complex laboratory protocol and diligent quality management is recommended to avoid costly failure. Current wet-lab protocols for Hi-C library QC provide only a crude assay, while commonly used sequence-based QC methods demand a reference genome; the quality of which can skew results. We propose a new, reference-free approach for Hi-C library quality assessment that requires only a modest amount of sequencing data. The algorithm builds upon the observation that proximity ligation events are likely to create k -mers that would not naturally occur in the sample. Our software tool (qc3C) is to our knowledge the first to implement a reference-free Hi-C QC tool, and also provides reference-based QC, enabling Hi-C to be more easily applied to non-model organisms and environmental samples. We characterise the accuracy of the new algorithm on simulated and real datasets and compare it to reference-based methods.

scholarly journals Accurate allele frequencies from ultra-low coverage pool-seq samples in evolve-and-resequence experiments

2018 ◽  
Author(s):  
Susanne Tilk ◽  
Alan Bergland ◽  
Aaron Goodman ◽  
Paul Schmidt ◽  
Dmitri Petrov ◽  
...  
Keyword(s):  
Allele Frequency ◽  
Model Organism ◽  
Software Tool ◽  
Allele Frequencies ◽  
Model Organisms ◽  
Sequencing Data ◽  
High Coverage ◽  
Next Generation Sequencing Technology ◽  
Low Coverage ◽  
Pooled Samples

AbstractEvolve-and-resequence (E+R) experiments leverage next-generation sequencing technology to track the allele frequency dynamics of populations as they evolve. While previous work has shown that adaptive alleles can be detected by comparing frequency trajectories from many replicate populations, this power comes at the expense of high-coverage (>100x) sequencing of many pooled samples, which can be cost-prohibitive. Here, we show that accurate estimates of allele frequencies can be achieved with very shallow sequencing depths (<5x) via inference of known founder haplotypes in small genomic windows. This technique can be used to efficiently estimate frequencies for any number of bi-allelic SNPs in populations of any model organism founded with sequenced homozygous strains. Using both experimentally-pooled and simulated samples of Drosophila melanogaster, we show that haplotype inference can improve allele frequency accuracy by orders of magnitude for up to 50 generations of recombination, and is robust to moderate levels of missing data, as well as different selection regimes. Finally, we show that a simple linear model generated from these simulations can predict the accuracy of haplotype-derived allele frequencies in other model organisms and experimental designs. To make these results broadly accessible for use in E+R experiments, we introduce HAF-pipe, an open-source software tool for calculating haplotype-derived allele frequencies from raw sequencing data. Ultimately, by reducing sequencing costs without sacrificing accuracy, our method facilitates E+R designs with higher replication and resolution, and thereby, increased power to detect adaptive alleles.

scholarly journals Detection and application of genome-wide variations in peach for association and genetic relationship analysis

BMC Genetics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Liping Guan ◽  
Ke Cao ◽  
Yong Li ◽  
Jian Guo ◽  
Qiang Xu ◽  
...  
Keyword(s):  
Genetic Relationship ◽  
Dna Markers ◽  
High Throughput Sequencing ◽  
Prunus Persica ◽  
Genetic Research ◽  
Diploid Species ◽  
Sequencing Data ◽  
Relationship Analysis ◽  
Genome Wide ◽  
A Genome

Abstract Background Peach (Prunus persica L.) is a diploid species and model plant of the Rosaceae family. In the past decade, significant progress has been made in peach genetic research via DNA markers, but the number of these markers remains limited. Results In this study, we performed a genome-wide DNA markers detection based on sequencing data of six distantly related peach accessions. A total of 650,693~1,053,547 single nucleotide polymorphisms (SNPs), 114,227~178,968 small insertion/deletions (InDels), 8386~12,298 structure variants (SVs), 2111~2581 copy number variants (CNVs) and 229,357~346,940 simple sequence repeats (SSRs) were detected and annotated. To demonstrate the application of DNA markers, 944 SNPs were filtered for association study of fruit ripening time and 15 highly polymorphic SSRs were selected to analyze the genetic relationship among 221 accessions. Conclusions The results showed that the use of high-throughput sequencing to develop DNA markers is fast and effective. Comprehensive identification of DNA markers, including SVs and SSRs, would be of benefit to genetic diversity evaluation, genetic mapping, and molecular breeding of peach.

scholarly journals Transcriptome Analysis in Domesticated Species: Challenges and Strategies

2015 ◽  
Vol 9S4 ◽  
pp. BBI.S29334 ◽  
Author(s):  
Jessica P. Hekman ◽  
Jennifer L Johnson ◽  
Anna V. Kukekova
Keyword(s):  
Complex Traits ◽  
Gene Networks ◽  
Association Studies ◽  
Cultural Value ◽  
Genomic Research ◽  
Model Organisms ◽  
Genome Wide Association Studies ◽  
Rna Seq ◽  
Genome Wide ◽  
Genome Assemblies

Domesticated species occupy a special place in the human world due to their economic and cultural value. In the era of genomic research, domesticated species provide unique advantages for investigation of diseases and complex phenotypes. RNA sequencing, or RNA-seq, has recently emerged as a new approach for studying transcriptional activity of the whole genome, changing the focus from individual genes to gene networks. RNA-seq analysis in domesticated species may complement genome-wide association studies of complex traits with economic importance or direct relevance to biomedical research. However, RNA-seq studies are more challenging in domesticated species than in model organisms. These challenges are at least in part associated with the lack of quality genome assemblies for some domesticated species and the absence of genome assemblies for others. In this review, we discuss strategies for analyzing RNA-seq data, focusing particularly on questions and examples relevant to domesticated species.

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 Detection and application of genome-wide variations in peach for association and genetic relationship analysis

2019 ◽  
Author(s):  
Liping Guan ◽  
ke Cao ◽  
yong Li ◽  
jian guo ◽  
qiang xu ◽  
...  
Keyword(s):  
Genetic Relationship ◽  
Dna Markers ◽  
High Throughput Sequencing ◽  
Prunus Persica ◽  
Genetic Research ◽  
Diploid Species ◽  
Sequencing Data ◽  
Relationship Analysis ◽  
Genome Wide ◽  
A Genome

Abstract Background: Peach (Prunus persica L.) is a diploid species and model plant of the Rosaceae family. In the past decade, significant progress has been made in peach genetic research via DNA markers, but the number of these markers remains limited. Results: In this study, we performed a genome-wide DNA markers detection based on sequencing data of six distantly related peach accessions. A total of 650,693~1,053,547 single nucleotide polymorphisms (SNPs), 114,227~178,968 small insertion/deletions (InDels), 8,386~12,298 structure variants (SVs), 2,111~2,581 copy number variants (CNVs) and 229,357~346,940 simple sequence repeats (SSRs) were detected and annotated. To demonstrate the application of DNA markers, 944 SNPs were filtered for association study of fruit ripening time and 15 highly polymorphic SSRs were selected to analyze the genetic relationship among 221 accessions. Conclusions: The results showed that the use of high-throughput sequencing to develop DNA markers is fast and effective. Comprehensive identification of DNA markers, including SVs and SSRs, would be of benefit to genetic diversity evaluation, genetic mapping, and molecular breeding of peach.

scholarly journals Detection and application of genome-wide variations in peach for association and genetic relationship analysis

2019 ◽  
Author(s):  
Liping Guan ◽  
ke Cao ◽  
yong Li ◽  
jian guo ◽  
qiang xu ◽  
...  
Keyword(s):  
Genetic Relationship ◽  
Dna Markers ◽  
High Throughput Sequencing ◽  
Prunus Persica ◽  
Genetic Research ◽  
Diploid Species ◽  
Sequencing Data ◽  
Relationship Analysis ◽  
Genome Wide ◽  
A Genome

Abstract Abstract Background: Peach (Prunus persica L.) is a diploid species and model plant of the Rosaceae family. In the past decade, significant progress has been made in peach genetic research via DNA markers, but the number of these markers remains limited. Results: In this study, we performed a genome-wide DNA markers detection based on sequencing data of six distantly related peach accessions. A total of 650,693~1,053,547 single nucleotide polymorphisms (SNPs), 114,227~178,968 small insertion/deletions (InDels), 8,386~12,298 structure variants (SVs), 2,111~2,581 copy number variants (CNVs) and 229,357~346,940 simple sequence repeats (SSRs) were detected and annotated. To demonstrate the application of DNA markers, 944 SNPs were filtered for association study of fruit ripening time and 15 highly polymorphic SSRs were selected to analyze the genetic relationship among 221 accessions. Conclusions: The results showed that the use of high-throughput sequencing to develop DNA markers is fast and effective. Comprehensive identification of DNA markers, including SVs and SSRs, would be of benefit to genetic diversity evaluation, genetic mapping, and molecular breeding of peach.

scholarly journals Identifying contamination with advanced visualization and analysis practices: metagenomic approaches for eukaryotic genome assemblies

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e1839 ◽  
Author(s):  
Tom O. Delmont ◽  
A. Murat Eren
Keyword(s):  
High Throughput Sequencing ◽  
Draft Genome ◽  
Cost Effective ◽  
Single Copy ◽  
Eukaryotic Genome ◽  
Sequencing Data ◽  
Bacterial Genomes ◽  
Long Read ◽  
Domains Of Life ◽  
Genome Assemblies

High-throughput sequencing provides a fast and cost-effective mean to recover genomes of organisms from all domains of life. However, adequate curation of the assembly results against potential contamination of non-target organisms requires advanced bioinformatics approaches and practices. Here, we re-analyzed the sequencing data generated for the tardigradeHypsibius dujardini,and created a holistic display of the eukaryotic genome assembly using DNA data originating from two groups and eleven sequencing libraries. By using bacterial single-copy genes, k-mer frequencies, and coverage values of scaffolds we could identify and characterize multiple near-complete bacterial genomes from the raw assembly, and curate a 182 Mbp draft genome forH. dujardinisupported by RNA-Seq data. Our results indicate that most contaminant scaffolds were assembled from Moleculo long-read libraries, and most of these contaminants have differed between library preparations. Our re-analysis shows that visualization and curation of eukaryotic genome assemblies can benefit from tools designed to address the needs of today’s microbiologists, who are constantly challenged by the difficulties associated with the identification of distinct microbial genomes in complex environmental metagenomes.

scholarly journals fluff: exploratory analysis and visualization of high-throughput sequencing data

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2209 ◽  
Author(s):  
Georgios Georgiou ◽  
Simon J. van Heeringen
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Developmental Stages ◽  
Command Line ◽  
Clustering Methods ◽  
Sequencing Data ◽  
Link Type ◽  
High Throughput Sequencing Data ◽  
Genome Wide ◽  
Genome Wide Data

Summary.In this article we describe fluff, a software package that allows for simple exploration, clustering and visualization of high-throughput sequencing data mapped to a reference genome. The package contains three command-line tools to generate publication-quality figures in an uncomplicated manner using sensible defaults. Genome-wide data can be aggregated, clustered and visualized in a heatmap, according to different clustering methods. This includes a predefined setting to identify dynamic clusters between different conditions or developmental stages. Alternatively, clustered data can be visualized in a bandplot. Finally, fluff includes a tool to generate genomic profiles. As command-line tools, the fluff programs can easily be integrated into standard analysis pipelines. The installation is straightforward and documentation is available athttp://fluff.readthedocs.org.Availability.fluff is implemented in Python and runs on Linux. The source code is freely available for download athttps://github.com/simonvh/fluff.

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