scholarly journals Repliscan: a tool for classifying replication timing regions

2016 ◽  
Author(s):  
Gregory J. Zynda ◽  
Jawon Song ◽  
Lorenzo Concia ◽  
Emily E. Wear ◽  
Linda Hanley-Bowdoin ◽  
...  
Keyword(s):  
Quality Control ◽  
Experimental Design ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Replication Timing ◽  
Coverage Density ◽  
Analysis Methods ◽  
Analysis Window ◽  
Label Incorporation ◽  
Data Points

AbstractBackgroundReplication timing experiments that use label incorporation and high throughput sequencing produce peaked data similar to ChIP-Seq experiments. However, the differences in experimental design, coverage density, and possible results make traditional ChIP-Seq analysis methods inappropriate for use with replication timing.ResultsTo accurately detect and classify regions of replication across the genome, we present Repliscan. Repliscan robustly normalizes, automatically removes outlying and uninformative data points, and classifies Repli-seq signals into discrete combinations of replication signatures. The quality control steps and self-fitting methods make Repliscan generally applicable and more robust than previous methods that classify regions based on thresholds.ConclusionsRepliscan is simple and effective to use on organisms with different genome sizes. Even with analysis window sizes as small as 1 kilobase, reliable profiles can be generated with as little as 2.4x coverage.

scholarly journals DNA-Based Herbal Teas’ Authentication: An ITS2 and psbA-trnH Multi-Marker DNA Metabarcoding Approach

Plants ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 2120
Author(s):  
Jessica Frigerio ◽  
Giulia Agostinetto ◽  
Valerio Mezzasalma ◽  
Fabrizio De De Mattia ◽  
Massimo Labra ◽  
...  
Keyword(s):  
Quality Control ◽  
Quantitative Analysis ◽  
Medicinal Plants ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
The Other ◽  
Plant Component ◽  
Identification Rate ◽  
Dna Metabarcoding ◽  
Therapeutic Properties

Medicinal plants have been widely used in traditional medicine due to their therapeutic properties. Although they are mostly used as herbal infusion and tincture, employment as ingredients of food supplements is increasing. However, fraud and adulteration are widespread issues. In our study, we aimed at evaluating DNA metabarcoding as a tool to identify product composition. In order to accomplish this, we analyzed fifteen commercial products with DNA metabarcoding, using two barcode regions: psbA-trnH and ITS2. Results showed that on average, 70% (44–100) of the declared ingredients have been identified. The ITS2 marker appears to identify more species (n = 60) than psbA-trnH (n = 35), with an ingredients’ identification rate of 52% versus 45%, respectively. Some species are identified only by one marker rather than the other. Additionally, in order to evaluate the quantitative ability of high-throughput sequencing (HTS) to compare the plant component to the corresponding assigned sequences, in the laboratory, we created six mock mixtures of plants starting both from biomass and gDNA. Our analysis also supports the application of DNA metabarcoding for a relative quantitative analysis. These results move towards the application of HTS analysis for studying the composition of herbal teas for medicinal plants’ traceability and quality control.

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 RNAseqPS: A Web Tool for Estimating Sample Size and Power for RNAseq Experiment

2014 ◽  
Vol 13s6 ◽  
pp. CIN.S17688 ◽  
Author(s):  
Yan Guo ◽  
Shilin Zhao ◽  
Chung-I Li ◽  
Quanhu Sheng ◽  
Yu Shyr
Keyword(s):  
Experimental Design ◽  
Sample Size ◽  
High Throughput ◽  
Power Analysis ◽  
High Throughput Sequencing ◽  
Genome Wide Association Study ◽  
Negative Binomial ◽  
Sample Size Calculation ◽  
Power Calculation ◽  
Biological Studies

Sample size and power determination is the first step in the experimental design of a successful study. Sample size and power calculation is required for applications for National Institutes of Health (NIH) funding. Sample size and power calculation is well established for traditional biological studies such as mouse model, genome wide association study (GWAS), and microarray studies. Recent developments in high-throughput sequencing technology have allowed RNAseq to replace microarray as the technology of choice for high-throughput gene expression profiling. However, the sample size and power analysis of RNAseq technology is an underdeveloped area. Here, we present RNAseqPS, an advanced online RNAseq power and sample size calculation tool based on the Poisson and negative binomial distributions. RNAseqPS was built using the Shiny package in R. It provides an interactive graphical user interface that allows the users to easily conduct sample size and power analysis for RNAseq experimental design. RNAseqPS can be accessed directly at http://cqs.mc.vanderbilt.edu/shiny/RNAseqPS/ .

scholarly journals htSeqTools: high-throughput sequencing quality control, processing and visualization in R

2011 ◽  
Vol 28 (4) ◽  
pp. 589-590 ◽  
Author(s):  
Evarist Planet ◽  
Camille Stephan-Otto Attolini ◽  
Oscar Reina ◽  
Oscar Flores ◽  
David Rossell
Keyword(s):  
Quality Control ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Sequencing Quality

scholarly journals Rapid high-resolution measurement of DNA replication timing by droplet digital PCR

2017 ◽  
Author(s):  
Dzmitry G. Batrakou ◽  
Emma D. Heron ◽  
Conrad A. Nieduszynski
Keyword(s):  
Dna Replication ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Replication Timing ◽  
Digital Pcr ◽  
Droplet Digital Pcr ◽  
Human Cells ◽  
Genome Replication ◽  
Multiple Loci ◽  
Dna Replication Timing

ABSTRACTGenomes are replicated in a reproducible temporal pattern. Current methods for assaying allele replication timing are time consuming and/or expensive. These include high-throughput sequencing which can be used to measure DNA copy number as a proxy for allele replication timing. Here, we use droplet digital PCR to study DNA replication timing at multiple loci in budding yeast and human cells. We establish that the method has temporal and spatial resolutions comparable to the high-throughput sequencing approaches, while being faster than alternative locus-specific methods. Furthermore, the approach is capable of allele discrimination. We apply this method to determine relative replication timing across timing transition zones in cultured human cells. Finally, multiple samples can be analysed in parallel, allowing us to rapidly screen kinetochore mutants for perturbation to centromere replication timing. Therefore, this approach is well suited to the study of locus-specific replication and the screening of cis- and trans-acting mutants to identify mechanisms that regulate local genome replication timing.

scholarly journals HTSQualC is a flexible and one-step quality control software for high-throughput sequencing data analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Renesh Bedre ◽  
Carlos Avila ◽  
Kranthi Mandadi
Keyword(s):  
Quality Control ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Science Research ◽  
Control Analysis ◽  
Sequencing Data ◽  
Quality Control Analysis ◽  
High Throughput Sequencing Data ◽  
One Step ◽  
Automated Quality Control

AbstractUse of high-throughput sequencing (HTS) has become indispensable in life science research. Raw HTS data contains several sequencing artifacts, and as a first step it is imperative to remove the artifacts for reliable downstream bioinformatics analysis. Although there are multiple stand-alone tools available that can perform the various quality control steps separately, availability of an integrated tool that can allow one-step, automated quality control analysis of HTS datasets will significantly enhance handling large number of samples parallelly. Here, we developed HTSQualC, a stand-alone, flexible, and easy-to-use software for one-step quality control analysis of raw HTS data. HTSQualC can evaluate HTS data quality and perform filtering and trimming analysis in a single run. We evaluated the performance of HTSQualC for conducting batch analysis of HTS datasets with 322 samples with an average ~ 1 M (paired end) sequence reads per sample. HTSQualC accomplished the QC analysis in ~ 3 h in distributed mode and ~ 31 h in shared mode, thus underscoring its utility and robust performance. In addition to command-line execution, we integrated HTSQualC into the free, open-source, CyVerse cyberinfrastructure resource as a GUI interface, for wider access to experimental biologists who have limited computational resources and/or programming abilities.

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