scholarly journals HiCBricks: building blocks for efficient handling of large Hi-C datasets

2019 ◽  
Author(s):  
Koustav Pal ◽  
Ilario Tagliaferri ◽  
Carmen Maria Livi ◽  
Francesco Ferrari
Keyword(s):  
High Throughput Sequencing ◽  
Building Blocks ◽  
Efficient Solutions ◽  
Quality Data ◽  
Supplementary Information ◽  
Complex Data ◽  
High Quality Data ◽  
Chromosome Conformation ◽  
Genome Wide ◽  
User Friendly

Abstract Summary Genome-wide chromosome conformation capture based on high-throughput sequencing (Hi-C) has been widely adopted to study chromatin architecture by generating datasets of ever-increasing complexity and size. HiCBricks offers user-friendly and efficient solutions for handling large high-resolution Hi-C datasets. The package provides an R/Bioconductor framework with the bricks to build more complex data analysis pipelines and algorithms. HiCBricks already incorporates functions for calling domain boundaries and functions for high-quality data visualization. Availability and implementation http://bioconductor.org/packages/devel/bioc/html/HiCBricks.html. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Advantages of using graph databases to explore chromatin conformation capture experiments

2021 ◽  
Vol 22 (S2) ◽  
Author(s):  
Daniele D’Agostino ◽  
Pietro Liò ◽  
Marco Aldinucci ◽  
Ivan Merelli
Keyword(s):  
Web Application ◽  
High Throughput Sequencing ◽  
Cell Types ◽  
Graph Database ◽  
Graph Databases ◽  
Sources Of Information ◽  
Chromosome Conformation ◽  
Wide Scale ◽  
User Friendly ◽  
Different Cell Types

Abstract Background High-throughput sequencing Chromosome Conformation Capture (Hi-C) allows the study of DNA interactions and 3D chromosome folding at the genome-wide scale. Usually, these data are represented as matrices describing the binary contacts among the different chromosome regions. On the other hand, a graph-based representation can be advantageous to describe the complex topology achieved by the DNA in the nucleus of eukaryotic cells. Methods Here we discuss the use of a graph database for storing and analysing data achieved by performing Hi-C experiments. The main issue is the size of the produced data and, working with a graph-based representation, the consequent necessity of adequately managing a large number of edges (contacts) connecting nodes (genes), which represents the sources of information. For this, currently available graph visualisation tools and libraries fall short with Hi-C data. The use of graph databases, instead, supports both the analysis and the visualisation of the spatial pattern present in Hi-C data, in particular for comparing different experiments or for re-mapping omics data in a space-aware context efficiently. In particular, the possibility of describing graphs through statistical indicators and, even more, the capability of correlating them through statistical distributions allows highlighting similarities and differences among different Hi-C experiments, in different cell conditions or different cell types. Results These concepts have been implemented in NeoHiC, an open-source and user-friendly web application for the progressive visualisation and analysis of Hi-C networks based on the use of the Neo4j graph database (version 3.5). Conclusion With the accumulation of more experiments, the tool will provide invaluable support to compare neighbours of genes across experiments and conditions, helping in highlighting changes in functional domains and identifying new co-organised genomic compartments.

scholarly journals Current practice in plankton metabarcoding: optimization and error management

2019 ◽  
Vol 41 (5) ◽  
pp. 571-582 ◽  
Author(s):  
Luciana F Santoferrara
Keyword(s):  
High Throughput Sequencing ◽  
Current Practice ◽  
Research Question ◽  
Quality Data ◽  
Error Management ◽  
False Negatives ◽  
High Quality Data ◽  
Alpha And Beta Diversity ◽  
Downstream Analysis ◽  
Study Designs

Abstract High-throughput sequencing of a targeted genetic marker is being widely used to analyze biodiversity across taxa and environments. Amid a multitude of exciting findings, scientists have also identified and addressed technical and biological limitations. Improved study designs and alternative sampling, lab and bioinformatic procedures have progressively enhanced data quality, but some problems persist. This article provides a framework to recognize and bypass the main types of errors that can affect metabarcoding data: false negatives, false positives, artifactual variants, disproportions and incomplete or incorrect taxonomic identifications. It is crucial to discern potential error impacts on different ecological parameters (e.g. taxon distribution, community structure, alpha and beta-diversity), as error management implies compromises and is thus directed by the research question. Synthesis of multiple plankton metabarcoding evaluations (mock sample sequencing or microscope comparisons) shows that high-quality data for qualitative and some semiquantitative goals can be achieved by implementing three checkpoints: first, rigorous protocol optimization; second, error minimization; and third, downstream analysis that considers potentially remaining biases. Conclusions inform us about the reliability of metabarcoding for plankton studies and, because plankton provides unique chances to compare genotypes and phenotypes, the robustness of this method in general.

scholarly journals CUT&RUN: Targeted in situ genome-wide profiling with high efficiency for low cell numbers

2017 ◽  
Author(s):  
Peter J. Skene ◽  
Steven Henikoff
Keyword(s):  
Chromatin Immunoprecipitation ◽  
High Efficiency ◽  
Specific Protein ◽  
Micrococcal Nuclease ◽  
Quality Data ◽  
High Quality Data ◽  
Left Behind ◽  
Dna Accessibility ◽  
Genome Wide

SUMMARYCleavage Under Targets and Release Using Nuclease (CUT&RUN) is an epigenomic profiling strategy in which antibody-targeted controlled cleavage by micrococcal nuclease releases specific protein-DNA complexes into the supernatant for paired-end DNA sequencing. As only the targeted fragments enter into solution, and the vast majority of DNA is left behind, CUT&RUN has exceptionally low background levels. CUT&RUN outperforms the most widely-used Chromatin Immunoprecipitation (ChIP) protocols in resolution, signal-to-noise, and depth of sequencing required. In contrast to ChIP, CUT&RUN is free of solubility and DNA accessibility artifacts and can be used to profile insoluble chromatin and to detect long-range 3D contacts without cross-linking. Here we present an improved CUT&RUN protocol that does not require isolation of nuclei and provides high-quality data starting with only 100 cells for a histone modification and 1000 cells for a transcription factor. From cells to purified DNA CUT&RUN requires less than a day at the lab bench.

scholarly journals Accurate prediction of genome-wide RNA secondary structure profile based on extreme gradient boosting

2020 ◽  
Vol 36 (17) ◽  
pp. 4576-4582
Author(s):  
Yaobin Ke ◽  
Jiahua Rao ◽  
Huiying Zhao ◽  
Yutong Lu ◽  
Nong Xiao ◽  
...  
Keyword(s):  
Secondary Structure ◽  
High Throughput ◽  
Rna Secondary Structure ◽  
High Throughput Sequencing ◽  
Supplementary Information ◽  
Gradient Boosting ◽  
Synonymous Mutations ◽  
Periodic Distribution ◽  
Genome Wide ◽  
Extreme Gradient Boosting

Abstract Motivation RNA secondary structure plays a vital role in fundamental cellular processes, and identification of RNA secondary structure is a key step to understand RNA functions. Recently, a few experimental methods were developed to profile genome-wide RNA secondary structure, i.e. the pairing probability of each nucleotide, through high-throughput sequencing techniques. However, these high-throughput methods have low precision and cannot cover all nucleotides due to limited sequencing coverage. Results Here, we have developed a new method for the prediction of genome-wide RNA secondary structure profile from RNA sequence based on the extreme gradient boosting technique. The method achieves predictions with areas under the receiver operating characteristic curve (AUC) >0.9 on three different datasets, and AUC of 0.888 by another independent test on the recently released Zika virus data. These AUCs are consistently >5% greater than those by the CROSS method recently developed based on a shallow neural network. Further analysis on the 1000 Genome Project data showed that our predicted unpaired probabilities are highly correlated (>0.8) with the minor allele frequencies at synonymous, non-synonymous mutations, and mutations in untranslated regions, which were higher than those generated by RNAplfold. Moreover, the prediction over all human mRNA indicated a consistent result with previous observation that there is a periodic distribution of unpaired probability on codons. The accurate predictions by our method indicate that such model trained on genome-wide experimental data might be an alternative for analytical methods. Availability and implementation The GRASP is available for academic use at https://github.com/sysu-yanglab/GRASP. Supplementary information Supplementary data are available online.

scholarly journals bWGR: Bayesian whole-genome regression

2019 ◽  
Author(s):  
Alencar Xavier ◽  
William M Muir ◽  
Katy M Rainey
Keyword(s):  
Bayesian Methods ◽  
Expectation Maximization ◽  
Complex Traits ◽  
Hierarchical Models ◽  
R Package ◽  
Supplementary Information ◽  
Whole Genome ◽  
Regression Methods ◽  
Genome Wide ◽  
User Friendly

AbstractMotivationWhole-genome regressions methods represent a key framework for genome-wide prediction, cross-validation studies and association analysis. The bWGR offers a compendium of Bayesian methods with various priors available, allowing users to predict complex traits with different genetic architectures.ResultsHere we introduce bWGR, an R package that enables users to efficient fit and cross-validate Bayesian and likelihood whole-genome regression methods. It implements a series of methods referred to as the Bayesian alphabet under the traditional Gibbs sampling and optimized expectation-maximization. The package also enables fitting efficient multivariate models and complex hierarchical models. The package is user-friendly and computational efficient.Availability and implementationbWGR is an R package available in the CRAN repository. It can be installed in R by typing: install.packages(‘bWGR’).Supplementary informationSupplementary data are available at Bioinformatics online.

scholarly journals LabxDB: versatile databases for genomic sequencing and lab management

2020 ◽  
Vol 36 (16) ◽  
pp. 4530-4531 ◽  
Author(s):  
Charles E Vejnar ◽  
Antonio J Giraldez
Keyword(s):  
High Throughput Sequencing ◽  
Data Access ◽  
Supplementary Information ◽  
Genomic Sequencing ◽  
Multiple User ◽  
Experimental Laboratory ◽  
User Access ◽  
Flexible Framework ◽  
Rest Api ◽  
User Friendly

Abstract Summary Experimental laboratory management and data-driven science require centralized software for sharing information, such as lab collections or genomic sequencing datasets. Although database servers such as PostgreSQL can store such information with multiple-user access, they lack user-friendly graphical and programmatic interfaces for easy data access and inputting. We developed LabxDB, a versatile open-source solution for organizing and sharing structured data. We provide several out-of-the-box databases for deployment in the cloud including simple mutant or plasmid collections and purchase-tracking databases. We also developed a high-throughput sequencing (HTS) database, LabxDB seq, dedicated to storage of hierarchical sample annotations. Scientists can import their own or publicly available HTS data into LabxDB seq to manage them from production to publication. Using LabxDB’s programmatic access (REST API), annotations can be easily integrated into bioinformatics pipelines. LabxDB is modular, offering a flexible framework that scientists can leverage to build new database interfaces adapted to their needs. Availability and implementation LabxDB is available at https://gitlab.com/vejnar/labxdb and https://labxdb.vejnar.org for documentation. LabxDB is licensed under the terms of the Mozilla Public License 2.0. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals ANGSD-wrapper: utilities for analyzing next generation sequencing data

2016 ◽  
Author(s):  
Arun Durvasula ◽  
Paul J Hoffman ◽  
Tyler V Kent ◽  
Chaochih Liu ◽  
Thomas J Y Kono ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Molecular Ecology ◽  
Principal Component ◽  
Next Generation Sequencing Data ◽  
Sequencing Data ◽  
Genome Data ◽  
High Throughput Sequencing Data ◽  
Genome Wide ◽  
User Friendly

High throughput sequencing has changed many aspects of population genetics, molecular ecology, and related fields, affecting both experimental design and data analysis. The software package ANGSD allows users to perform a number of population genetic analyses on high-throughput sequencing data. ANGSD uses probabilistic approaches to calculate genome-wide descriptive statistics. The package makes use of genotype likelihood estimates rather than SNP calls and is specifically designed to produce more accurate results for samples with low sequencing depth. ANGSD makes use of full genome data while handling a wide array of sampling and experimental designs. Here we present ANGSD-wrapper, a set of wrapper scripts that provide a user-friendly interface for running ANGSD and visualizing results. ANGSD-wrapper supports multiple types of analyses including esti- mates of nucleotide sequence diversity and performing neutrality tests, principal component analysis, estimation of admixture proportions for individuals samples, and calculation of statistics that quantify recent introgression. ANGSD-wrapper also provides interactive graphing of ANGSD results to enhance data exploration. We demonstrate the usefulness of ANGSD-wrapper by analyzing resequencing data from populations of wild and domesticated Zea. ANGSD-wrapper is freely available from https://github.com/mojaveazure/angsd-wrapper.

scholarly journals Elimination of PCR duplicates in RNA-seq and small RNA-seq using unique molecular identifiers

2018 ◽  
Author(s):  
Yu Fu ◽  
Pei-Hsuan Wu ◽  
Timothy Beane ◽  
Phillip D. Zamore ◽  
Zhiping Weng
Keyword(s):  
Single Molecule ◽  
Small Rna ◽  
High Throughput Sequencing ◽  
Starting Material ◽  
Quality Data ◽  
Rna Seq ◽  
High Quality Data ◽  
Pcr Duplicates ◽  
Substantial Bias ◽  
And Function

AbstractRNA-seq and small RNA-seq are powerful, quantitative tools to study gene regulation and function. Common high-throughput sequencing methods rely on polymerase chain reaction (PCR) to expand the starting material, but not every molecule amplifies equally, causing some to be overrepresented. Unique molecular identifiers (UMIs) can be used to distinguish undesirable PCR duplicates derived from a single molecule and identical but biologically meaningful reads from different molecules. We have incorporated UMIs into RNA-seq and small RNA-seq protocols and developed tools to analyze the resulting data. Our UMIs contain stretches of random nucleotides whose lengths sufficiently capture diverse molecule species in both RNA-seq and small RNA-seq libraries generated from mouse testis. Our approach yields high-quality data while allowing unique tagging of all molecules in high-depth libraries. Using simulated and real datasets, we demonstrate that our methods increase the reproducibility of RNA-seq and small RNA-seq data. Notably, we find that the amount of starting material and sequencing depth, but not the number of PCR cycles, determine PCR duplicate frequency. Finally, we show that computational removal of PCR duplicates based only on their mapping coordinates introduces substantial bias into data analysis.

scholarly journals Three-dimensional chromatin in infectious disease—A role for gene regulation and pathogenicity?

2021 ◽  
Vol 17 (2) ◽  
pp. e1009207
Author(s):  
Sage Z. Davis ◽  
Thomas Hollin ◽  
Todd Lenz ◽  
Karine G. Le Roch
Keyword(s):  
Infectious Disease ◽  
Gene Regulation ◽  
High Throughput Sequencing ◽  
Three Dimensional ◽  
Nuclear Architecture ◽  
Host Cells ◽  
Chromosome Conformation ◽  
Infectious Disease Research ◽  
Genome Wide

The recent Coronavirus Disease 2019 pandemic has once again reminded us the importance of understanding infectious diseases. One important but understudied area in infectious disease research is the role of nuclear architecture or the physical arrangement of the genome in the nucleus in controlling gene regulation and pathogenicity. Recent advances in research methods, such as Genome-wide chromosome conformation capture using high-throughput sequencing (Hi-C), have allowed for easier analysis of nuclear architecture and chromosomal reorganization in both the infectious disease agents themselves as well as in their host cells. This review will discuss broadly on what is known about nuclear architecture in infectious disease, with an emphasis on chromosomal reorganization, and briefly discuss what steps are required next in the field.

deMeta: Removing sub-studies from meta-analysis of genome wide association studies (GWAS)

2020 ◽  
Author(s):  
Jiangming Sun ◽  
Yunpeng Wang
Keyword(s):  
Association Studies ◽  
Meta Analysis ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Computationally Efficient ◽  
Genome Wide ◽  
Quantile Plots ◽  
User Friendly ◽  
The Impact

ABSTRACTSummaryPost-GWAS studies using the results from large consortium meta-analysis often need to correctly take care of the overlapping sample issue. The gold standard approach for resolving this issue is to reperform the GWAS or meta-analysis excluding the overlapped participants. However, such approach is time-consuming and, sometimes, restricted by the available data. deMeta provides a user friendly and computationally efficient command-line implementation for removing the effect of a contributing sub-study to a consortium from the meta-analysis results. Only the summary statistics of the meta-analysis the sub-study to be removed are required. In addition, deMeta can generate contrasting Manhattan and quantile-quantile plots for users to visualize the impact of the sub-study on the meta-analysis results.Availability and ImplementationThe python source code, examples and documentations of deMeta are publicly available at https://github.com/Computational-NeuroGenetics/[email protected] (J. Sun); [email protected] (Y. Wang)Supplementary informationNone.

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