scholarly journals csaw: a Bioconductor package for differential binding analysis of ChIP-seq data using sliding windows

2015 ◽  
Vol 44 (5) ◽  
pp. e45-e45 ◽  
Author(s):  
Aaron T.L. Lun ◽  
Gordon K. Smyth
Keyword(s):  
De Novo ◽  
Massively Parallel Sequencing ◽  
Real Data ◽  
Sequencing Data ◽  
Scientific Application ◽  
Sliding Windows ◽  
Treatment Conditions ◽  
Bioconductor Project ◽  
Differential Binding ◽  
Genomic Regions

Abstract Chromatin immunoprecipitation with massively parallel sequencing (ChIP-seq) is widely used to identify binding sites for a target protein in the genome. An important scientific application is to identify changes in protein binding between different treatment conditions, i.e. to detect differential binding. This can reveal potential mechanisms through which changes in binding may contribute to the treatment effect. The csaw package provides a framework for the de novo detection of differentially bound genomic regions. It uses a window-based strategy to summarize read counts across the genome. It exploits existing statistical software to test for significant differences in each window. Finally, it clusters windows into regions for output and controls the false discovery rate properly over all detected regions. The csaw package can handle arbitrarily complex experimental designs involving biological replicates. It can be applied to both transcription factor and histone mark datasets, and, more generally, to any type of sequencing data measuring genomic coverage. csaw performs favorably against existing methods for de novo DB analyses on both simulated and real data. csaw is implemented as a R software package and is freely available from the open-source Bioconductor project.

scholarly journals From reads to regions: a Bioconductor workflow to detect differential binding in ChIP-seq data

F1000Research ◽  
2016 ◽  
Vol 4 ◽  
pp. 1080 ◽  
Author(s):  
Aaron T. L. Lun ◽  
Gordon K. Smyth
Keyword(s):  
Massively Parallel Sequencing ◽  
Statistical Modelling ◽  
Data Sets ◽  
Biological Difference ◽  
Sliding Windows ◽  
Software Packages ◽  
Bioconductor Project ◽  
Differential Binding ◽  
Factor Data ◽  
Binding Intensity

Chromatin immunoprecipitation with massively parallel sequencing (ChIP-seq) is widely used to identify the genomic binding sites for protein of interest. Most conventional approaches to ChIP-seq data analysis involve the detection of the absolute presence (or absence) of a binding site. However, an alternative strategy is to identify changes in the binding intensity between two biological conditions, i.e., differential binding (DB). This may yield more relevant results than conventional analyses, as changes in binding can be associated with the biological difference being investigated. The aim of this article is to facilitate the implementation of DB analyses, by comprehensively describing a computational workflow for the detection of DB regions from ChIP-seq data. The workflow is based primarily on R software packages from the open-source Bioconductor project and covers all steps of the analysis pipeline, from alignment of read sequences to interpretation and visualization of putative DB regions. In particular, detection of DB regions will be conducted using the counts for sliding windows from the csaw package, with statistical modelling performed using methods in the edgeR package. Analyses will be demonstrated on real histone mark and transcription factor data sets. This will provide readers with practical usage examples that can be applied in their own studies.

scholarly journals From reads to regions: a Bioconductor workflow to detect differential binding in ChIP-seq data

F1000Research ◽  
2015 ◽  
Vol 4 ◽  
pp. 1080 ◽  
Author(s):  
Aaron T. L. Lun ◽  
Gordon K. Smyth
Keyword(s):  
Massively Parallel Sequencing ◽  
Statistical Modelling ◽  
Data Sets ◽  
Biological Difference ◽  
Sliding Windows ◽  
Software Packages ◽  
Bioconductor Project ◽  
Differential Binding ◽  
Factor Data ◽  
Binding Intensity

Chromatin immunoprecipitation with massively parallel sequencing (ChIP-seq) is widely used to identify the genomic binding sites for protein of interest. Most conventional approaches to ChIP-seq data analysis involve the detection of the absolute presence (or absence) of a binding site. However, an alternative strategy is to identify changes in the binding intensity between two biological conditions, i.e., differential binding (DB). This may yield more relevant results than conventional analyses, as changes in binding can be associated with the biological difference being investigated. The aim of this article is to facilitate the implementation of DB analyses, by comprehensively describing a computational workflow for the detection of DB regions from ChIP-seq data. The workflow is based primarily on R software packages from the open-source Bioconductor project and covers all steps of the analysis pipeline, from alignment of read sequences to interpretation and visualization of putative DB regions. In particular, detection of DB regions will be conducted using the counts for sliding windows from the csaw package, with statistical modelling performed using methods in the edgeR package. Analyses will be demonstrated on real histone mark and transcription factor data sets. This will provide readers with practical usage examples that can be applied in their own studies.

scholarly journals CAMISIM: Simulating metagenomes and microbial communities

2018 ◽  
Author(s):  
Adrian Fritz ◽  
Peter Hofmann ◽  
Stephan Majda ◽  
Eik Dahms ◽  
Johannes Dröge ◽  
...  
Keyword(s):  
Microbial Communities ◽  
De Novo ◽  
Real Data ◽  
Small Data ◽  
Data Sets ◽  
Sequencing Data ◽  
Taxonomic Profiling ◽  
Benchmark Data ◽  
Sequencing Technologies ◽  
Wide Range

Shotgun metagenome data sets of microbial communities are highly diverse, not only due to the natural variation of the underlying biological systems, but also due to differences in laboratory protocols, replicate numbers, and sequencing technologies. Accordingly, to effectively assess the performance of metagenomic analysis software, a wide range of benchmark data sets are required. Here, we describe the CAMISIM microbial community and metagenome simulator. The software can model different microbial abundance profiles, multi-sample time series and differential abundance studies, includes real and simulated strain-level diversity, and generates second and third generation sequencing data from taxonomic profiles or de novo. Gold standards are created for sequence assembly, genome binning, taxonomic binning, and taxonomic profiling. CAMSIM generated the benchmark data sets of the first CAMI challenge. For two simulated multi-sample data sets of the human and mouse gut microbiomes we observed high functional congruence to the real data. As further applications, we investigated the effect of varying evolutionary genome divergence, sequencing depth, and read error profiles on two popular metagenome assemblers, MEGAHIT and metaSPAdes, on several thousand small data sets generated with CAMISIM. CAMISIM can simulate a wide variety of microbial communities and metagenome data sets together with truth standards for method evaluation. All data sets and the software are freely available at: https://github.com/CAMI-challenge/CAMISIM

scholarly journals Germline de novo mutation clusters arise during oocyte aging in genomic regions with increased double-strand break incidence

2017 ◽  
Author(s):  
Jakob M. Goldmann ◽  
Vladimir B. Seplyarskiy ◽  
Wendy S.W. Wong ◽  
Thierry Vilboux ◽  
Dale L. Bodian ◽  
...  
Keyword(s):  
De Novo ◽  
Underlying Mechanism ◽  
Strand Break ◽  
De Novo Mutation ◽  
Whole Genome Sequencing Data ◽  
Sequencing Data ◽  
Induced Mutations ◽  
Cancer Genomes ◽  
Oocyte Aging ◽  
Genomic Regions

Clustering of mutations has been found both in somatic mutations from cancer genomes and in germline de novo mutations (DNMs). We identified 1,755 clustered DNMs (cDNMs) within whole-genome sequencing data from 1,291 parent-offspring trios and investigated the underlying mutational mechanisms. We found that the number of clusters on the maternalallele was positively correlated with maternal age and that these consist of more individual mutations with larger intra-mutational distances compared to paternal clusters. More than 50% of maternal clusters were located on chromosomes 8, 9 and 16, in regions with an overall increased maternal mutation rate. Maternal clusters in these regions showed a distinct mutation signature characterized by C>G mutations. Finally, we found that maternal clusters associate with processes involving double-stranded-breaks (DSBs) such as meiotic gene conversions and de novo deletions events. These findings suggest accumulation of DSB-induced mutations throughout oocyte aging as an underlying mechanism leading to maternal mutation clusters.

scholarly journals Assessing 16S marker gene survey data analysis methods using mixtures of human stool sample DNA extracts

2018 ◽  
Author(s):  
Nathan D Olson ◽  
M. Senthil Kumar ◽  
Shan Li ◽  
Stephanie Hao ◽  
Winston Timp ◽  
...  
Keyword(s):  
16S Rrna ◽  
De Novo ◽  
Marker Gene ◽  
Real Data ◽  
Qualitative Assessment ◽  
Data Sets ◽  
Sequencing Data ◽  
Differential Abundance ◽  
Analysis Methods ◽  
Downstream Analysis

AbstractBackgroundAnalysis of 16S rRNA marker-gene surveys, used to characterize prokaryotic microbial communities, may be performed by numerous bioinformatic pipelines and downstream analysis methods. However, there is limited guidance on how to decide between methods, appropriate data sets and statistics for assessing these methods are needed. We developed a mixture dataset with real data complexity and an expected value for assessing 16S rRNA bioinformatic pipelines and downstream analysis methods. We generate an assessment dataset using a two-sample titration mixture design. The sequencing data were processed using multiple bioinformatic pipelines, i) DADA2 a sequence inference method, ii) Mothur a de novo clustering method, and iii) QIIME with open-reference clustering. The mixture dataset was used to qualitatively and quantitatively assess count tables generated using the pipelines.ResultsThe qualitative assessment was used to evalute features only present in unmixed samples and titrations. The abundance of Mothur and QIIME features specific to unmixed samples and titrations were explained by sampling alone. However, for DADA2 over a third of the unmixed sample and titration specific feature abundance could not be explained by sampling alone. The quantitative assessment evaluated pipeline performance by comparing observed to expected relative and differential abundance values. Overall the observed relative abundance and differential abundance values were consistent with the expected values. Though outlier features were observed across all pipelines.ConclusionsUsing a novel mixture dataset and assessment methods we quantitatively and qualitatively evaluated count tables generated using three bioinformatic pipelines. The dataset and methods developed for this study will serve as a valuable community resource for assessing 16S rRNA marker-gene survey bioinformatic methods.

scholarly journals A haplotype-aware de novo assembly of related individuals using pedigree graph

2019 ◽  
Author(s):  
Shilpa Garg ◽  
John Aach ◽  
Heng Li ◽  
Richard Durbin ◽  
George Church
Keyword(s):  
De Novo ◽  
Diploid Species ◽  
Real Data ◽  
Genome Project ◽  
Personal Genome ◽  
Sequencing Data ◽  
High Quality ◽  
Illumina Data ◽  
Long Read ◽  
Related Individuals

AbstractMotivationReconstructing high-quality haplotype-resolved assemblies for related individuals of various species has important applications in understanding Mendelian diseases along with evolutionary and comparative genomics. Through major genomics sequencing efforts such as the Personal Genome Project, the Vertebrate Genome Project (VGP), the Earth Biogenome Project (EBP) and the Genome in a Bottle project (GIAB), a variety of sequencing datasets from mother-father-child trios of various diploid species are becoming available.Current trio assembly approaches are not designed to incorporate long-read sequencing data from parents in a trio, and therefore require relatively high coverages of costly long-read data to produce high-quality assemblies. Thus, building a trio-aware assembler capable of producing accurate and chromosomal-scale diploid genomes in a pedigree, while being cost-effective in terms of sequencing costs, is a pressing need of the genomics community.ResultsWe present a novel pedigree-graph-based approach to diploid assembly using accurate Illumina data and long-read Pacific Biosciences (PacBio) data from all related individuals, thereby generalizing our previous work on single individuals. We demonstrate the effectiveness of our pedigree approach on a simulated trio of pseudo-diploid yeast genomes with different heterozygosity rates, and real data from Arabidopsis Thaliana. We show that we require as little as 30× coverage Illumina data and 15× PacBio data from each individual in a trio to generate chromosomal-scale phased assemblies. Additionally, we show that we can detect and phase variants from generated phased assemblies.Availabilityhttps://github.com/shilpagarg/[email protected], [email protected]

scholarly journals Haplotype Threading: Accurate Polyploid Phasing from Long Reads

2020 ◽  
Author(s):  
Sven D. Schrinner ◽  
Rebecca Serra Mari ◽  
Jana Ebler ◽  
Mikko Rautiainen ◽  
Lancelot Seillier ◽  
...  
Keyword(s):  
Scoring Function ◽  
Simulated Data ◽  
Real Data ◽  
Error Rates ◽  
Sequencing Data ◽  
Data Set ◽  
Current State ◽  
Long Reads ◽  
History Of ◽  
Genomic Regions

AbstractResolving genomes at haplotype level is crucial for understanding the evolutionary history of polyploid species and for designing advanced breeding strategies. As a highly complex computational problem, polyploid phasing still presents considerable challenges, especially in regions of collapsing haplotypes.We present WhatsHap polyphase, a novel two-stage approach that addresses these challenges by (i) clustering reads using a position-dependent scoring function and (ii) threading the haplotypes through the clusters by dynamic programming. We demonstrate on a simulated data set that this results in accurate haplotypes with switch error rates that are around three times lower than those obtainable by the current state-of-the-art and even around seven times lower in regions of collapsing haplotypes. Using a real data set comprising long and short read tetraploid potato sequencing data we show that WhatsHap polyphase is able to phase the majority of the potato genes after error correction, which enables the assembly of local genomic regions of interest at haplotype level. Our algorithm is implemented as part of the widely used open source tool WhatsHap and ready to be included in production settings.

scholarly journals LDscaff: LD-based scaffolding of de novo genome assemblies

2020 ◽  
Vol 21 (S21) ◽  
Author(s):  
Zicheng Zhao ◽  
Yingxiao Zhou ◽  
Shuai Wang ◽  
Xiuqing Zhang ◽  
Changfa Wang ◽  
...  
Keyword(s):  
Genome Assembly ◽  
De Novo ◽  
Genetic Recombination ◽  
Draft Genome ◽  
Simulated Data ◽  
Population Data ◽  
Real Data ◽  
Physical Distance ◽  
Whole Genome Sequencing Data ◽  
Sequencing Data

Abstract Background Genome assembly is fundamental for de novo genome analysis. Hybrid assembly, utilizing various sequencing technologies increases both contiguity and accuracy. While such approaches require extra costly sequencing efforts, the information provided millions of existed whole-genome sequencing data have not been fully utilized to resolve the task of scaffolding. Genetic recombination patterns in population data indicate non-random association among alleles at different loci, can provide physical distance signals to guide scaffolding. Results In this paper, we propose LDscaff for draft genome assembly incorporating linkage disequilibrium information in population data. We evaluated the performance of our method with both simulated data and real data. We simulated scaffolds by splitting the pig reference genome and reassembled them. Gaps between scaffolds were introduced ranging from 0 to 100 KB. The genome misassembly rate is 2.43% when there is no gap. Then we implemented our method to refine the Giant Panda genome and the donkey genome, which are purely assembled by NGS data. After LDscaff treatment, the resulting Panda assembly has scaffold N50 of 3.6 MB, 2.5 times larger than the original N50 (1.3 MB). The re-assembled donkey assembly has an improved N50 length of 32.1 MB from 23.8 MB. Conclusions Our method effectively improves the assemblies with existed re-sequencing data, and is an potential alternative to the existing assemblers required for the collection of new data.

scholarly journals Quantifying RNA Synthesis at Rate-Limiting Steps of Transcription Using Nascent RNA-Sequencing Data

2021 ◽  
Author(s):  
Adelina Rabenius ◽  
Sajitha Chandrakumaran ◽  
Lea Sistonen ◽  
Anniina Vihervaara
Keyword(s):  
Rna Sequencing ◽  
Rna Synthesis ◽  
De Novo ◽  
Regulatory Elements ◽  
Functional Genomic ◽  
Sequencing Data ◽  
Nascent Rna ◽  
Transcription Complexes ◽  
Nucleotide Resolution ◽  
Genomic Regions

Nascent RNA-sequencing tracks transcription at nucleotide resolution. The genomic distribution of engaged transcription complexes, in turn, uncovers functional genomic regions. Here, we provide data-analytical steps to 1) identify transcribed regulatory elements de novo genome-wide, 2) quantify engaged transcription complexes at enhancers, promoter-proximal regions, divergent transcripts, gene bodies and termination windows, and 3) measure distribution of transcription machineries and regulatory proteins across functional genomic regions. This protocol follows RNA synthesis and genome-regulation in mammals, as demonstrated in human K562 erythroleukemia cells.

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