scholarly journals RiboVIEW: a computational framework for visualization, quality control and statistical analysis of ribosome profiling data

2019 ◽  
Vol 48 (2) ◽  
pp. e7-e7 ◽  
Author(s):  
Carine Legrand ◽  
Francesca Tuorto
Keyword(s):  
Quality Control ◽  
Statistical Analysis ◽  
Computational Analysis ◽  
High Throughput Sequencing ◽  
Ribosome Profiling ◽  
Confounding Factors ◽  
Batch Effects ◽  
Sequencing Data ◽  
Computational Framework ◽  
Genome Wide

Abstract Recently, newly developed ribosome profiling methods based on high-throughput sequencing of ribosome-protected mRNA footprints allow to study genome-wide translational changes in detail. However, computational analysis of the sequencing data still represents a bottleneck for many laboratories. Further, specific pipelines for quality control and statistical analysis of ribosome profiling data, providing high levels of both accuracy and confidence, are currently lacking. In this study, we describe automated bioinformatic and statistical diagnoses to perform robust quality control of ribosome profiling data (RiboQC), to efficiently visualize ribosome positions and to estimate ribosome speed (RiboMine) in an unbiased way. We present an R pipeline to setup and undertake the analyses that offers the user an HTML page to scan own data regarding the following aspects: periodicity, ligation and digestion of footprints; reproducibility and batch effects of replicates; drug-related artifacts; unbiased codon enrichment including variability between mRNAs, for A, P and E sites; mining of some causal or confounding factors. We expect our pipeline to allow an optimal use of the wealth of information provided by ribosome profiling experiments.

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 PathoQC: Computationally Efficient Read Preprocessing and Quality Control for High-Throughput Sequencing Data Sets

2014 ◽  
Vol 13s1 ◽  
pp. CIN.S13890 ◽  
Author(s):  
Changjin Hong ◽  
Solaiappan Manimaran ◽  
William Evan Johnson
Keyword(s):  
Quality Control ◽  
High Throughput ◽  
High Performance ◽  
High Throughput Sequencing ◽  
Next Generation Sequencing Data ◽  
Data Sets ◽  
Sequencing Data ◽  
Computationally Efficient ◽  
High Throughput Sequencing Data ◽  
Downstream Analysis

Quality control and read preprocessing are critical steps in the analysis of data sets generated from high-throughput genomic screens. In the most extreme cases, improper preprocessing can negatively affect downstream analyses and may lead to incorrect biological conclusions. Here, we present PathoQC, a streamlined toolkit that seamlessly combines the benefits of several popular quality control software approaches for preprocessing next-generation sequencing data. PathoQC provides a variety of quality control options appropriate for most high-throughput sequencing applications. PathoQC is primarily developed as a module in the PathoScope software suite for metagenomic analysis. However, PathoQC is also available as an open-source Python module that can run as a stand-alone application or can be easily integrated into any bioinformatics workflow. PathoQC achieves high performance by supporting parallel computation and is an effective tool that removes technical sequencing artifacts and facilitates robust downstream analysis. The PathoQC software package is available at http://sourceforge.net/projects/PathoScope/ .

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 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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