GenomeView: a next-generation genome browser

Abeel, Thomas; Parys, Thomas Van; Saeys, Yvan; Galagan, James E.; Peer, Yves Van de

doi:10.1093/nar/gkr995

Cited by 138 publications

(116 citation statements)

References 46 publications

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“…Individual variants can be checked either by visual inspection (using dedicated software packages such as IGV (Robinson et al, 2011), GenomeView (Abeel et al, 2012) or SAVANT (Fiume et al, 2010)), or by conducting a follow-up validation experiment using an independent technology, whereas the overall quality of the data set can be assessed using several simple metrics. First, the number of observed SNPs should be in line with the species' expected diversity.…”

Section: Quality Assessmentmentioning

confidence: 99%

From next-generation resequencing reads to a high-quality variant data set

Pfeifer

2016

Heredity

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Sequencing has revolutionized biology by permitting the analysis of genomic variation at an unprecedented resolution. Highthroughput sequencing is fast and inexpensive, making it accessible for a wide range of research topics. However, the produced data contain subtle but complex types of errors, biases and uncertainties that impose several statistical and computational challenges to the reliable detection of variants. To tap the full potential of high-throughput sequencing, a thorough understanding of the data produced as well as the available methodologies is required. Here, I review several commonly used methods for generating and processing next-generation resequencing data, discuss the influence of errors and biases together with their resulting implications for downstream analyses and provide general guidelines and recommendations for producing high-quality single-nucleotide polymorphism data sets from raw reads by highlighting several sophisticated reference-based methods representing the current state of the art. INTRODUCTIONSequencing has entered the scientific zeitgeist and the demand for novel sequences has never been greater, with applications spanning comparative genomics, clinical diagnostics and metagenomics, as well as agricultural, evolutionary, forensic and medical genetic studies. Several hundred species have already been completely sequenced (among them reference genomes for human and numerous major model organisms) (Pagani et al., 2012), shifting the focus of many scientific studies toward resequencing analyses in order to identify and catalog genetic variation in different individuals of a population. These population-scale studies permit insights into natural variation, inference on the demographic and selective history of a population, and variants identified in phenotypically distinct individuals can be used to dissect the relationship between genotype and phenotype.Until 2005, Sanger sequencing was the dominant technology, but it was prohibitively expensive and time consuming to routinely perform sequencing on a scale required to reach the scientific goals of many modern research projects. For these reasons, several massively parallel, high-throughput 'next-generation' sequencing (NGS) technologies have since been developed, permitting the analyses of genomes and their variation by being hundreds of times faster and over a thousand times cheaper than traditional Sanger sequencing (Metzker, 2010). Whereas the major limiting factor in the Sanger sequencing era was the experimental production of sequence data, data generation using NGS platforms is straightforward, shifting the bottleneck to downstream analyses, with computational costs often surpassing those of data production (Mardis, 2010). In fact, a multitude of bioinformatic algorithms is necessary to efficiently analyze the generated data sets and to answer biologically relevant questions. Thereby, the large amount of data with shorter read lengths, higher per-base error rates

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Section: Quality Assessmentmentioning

confidence: 99%

From next-generation resequencing reads to a high-quality variant data set

Pfeifer

2016

Heredity

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“…The entire set of peak-called regions can be accessed and downloaded at http://bioinformatics.psb.ugent.be/cig_data/ RegNet/ (see Methods). The GenomeView (Abeel et al, 2012) visualization also includes the DH sites (Zhang et al, 2012) discussed below.…”

Section: Construction Of An Experimental Arabidopsis Gene Regulatory mentioning

confidence: 99%

“…REV, AMS, and FLP/MYB88 were removed from the data set due to a very low number of peaks in the results, the lack of paired-end read processing in the computational pipeline, and an abnormally high fraction of peak regions near transposable elements (Supplemental Figure 2A), respectively. All experiments were visually inspected with GenomeView (Abeel et al, 2012), and all figures were made using matplotlib (Hunter, 2007).…”

Section: Chip-seq Processingmentioning

confidence: 99%

A Functional and Evolutionary Perspective on Transcription Factor Binding in Arabidopsis thaliana

et al. 2014

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Understanding the mechanisms underlying gene regulation is paramount to comprehend the translation from genotype to phenotype. The two are connected by gene expression, and it is generally thought that variation in transcription factor (TF) function is an important determinant of phenotypic evolution. We analyzed publicly available genome-wide chromatin immunoprecipitation experiments for 27 TFs in Arabidopsis thaliana and constructed an experimental network containing 46,619 regulatory interactions and 15,188 target genes. We identified hub targets and highly occupied target (HOT) regions, which are enriched for genes involved in development, stimulus responses, signaling, and gene regulatory processes in the currently profiled network. We provide several lines of evidence that TF binding at plant HOT regions is functional, in contrast to that in animals, and not merely the result of accessible chromatin. HOT regions harbor specific DNA motifs, are enriched for differentially expressed genes, and are often conserved across crucifers and dicots, even though they are not under higher levels of purifying selection than non-HOT regions. Distal bound regions are under purifying selection as well and are enriched for a chromatin state showing regulation by the Polycomb repressive complex. Gene expression complexity is positively correlated with the total number of bound TFs, revealing insights in the regulatory code for genes with different expression breadths. The integration of noncanonical and canonical DNA motif information yields new hypotheses on cobinding and tethering between specific TFs involved in flowering and light regulation.

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“…On this page, a complete overview per investigated region, upstream, downstream, or intron, is given for all retrieved binding sites per gene. Complementary, conserved binding sites also are visualized using the GenomeView genome browser for all 10 species (Abeel et al, 2012). On all Arabidopsis TF-encoding gene pages that have TFBS information, a tab was added containing the associated binding sites for that TF.…”

Section: Exploration and Visualization Of Plant Cnss Through The Plazmentioning

confidence: 99%

A Collection of Conserved Noncoding Sequences to Study Gene Regulation in Flowering Plants

Velde

Bel²,

Vaneechoutte³

et al. 2016

Plant Physiol.

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Transcription factors (TFs) regulate gene expression by binding cis-regulatory elements, of which the identification remains an ongoing challenge owing to the prevalence of large numbers of nonfunctional TF binding sites. Powerful comparative genomics methods, such as phylogenetic footprinting, can be used for the detection of conserved noncoding sequences (CNSs), which are functionally constrained and can greatly help in reducing the number of false-positive elements. In this study, we applied a phylogenetic footprinting approach for the identification of CNSs in 10 dicot plants, yielding 1,032,291 CNSs associated with 243,187 genes. To annotate CNSs with TF binding sites, we made use of binding site information for 642 TFs originating from 35 TF families in Arabidopsis (Arabidopsis thaliana). In three species, the identified CNSs were evaluated using TF chromatin immunoprecipitation sequencing data, resulting in significant overlap for the majority of data sets. To identify ultraconserved CNSs, we included genomes of additional plant families and identified 715 binding sites for 501 genes conserved in dicots, monocots, mosses, and green algae. Additionally, we found that genes that are part of conserved mini-regulons have a higher coherence in their expression profile than other divergent gene pairs. All identified CNSs were integrated in the PLAZA 3.0 Dicots comparative genomics platform (http://bioinformatics.psb.ugent.be/plaza/versions/plaza_v3_dicots/) together with new functionalities facilitating the exploration of conserved cis-regulatory elements and their associated genes. The availability of this data set in a user-friendly platform enables the exploration of functional noncoding DNA to study gene regulation in a variety of plant species, including crops.

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GenomeView: a next-generation genome browser

Cited by 138 publications

References 46 publications

From next-generation resequencing reads to a high-quality variant data set

From next-generation resequencing reads to a high-quality variant data set

A Functional and Evolutionary Perspective on Transcription Factor Binding in Arabidopsis thaliana

A Collection of Conserved Noncoding Sequences to Study Gene Regulation in Flowering Plants

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