BAMscale: quantification of DNA sequencing peaks and generation of scaled coverage tracks
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA543507 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR9083560_1.fastq.gz | Fastqsanger.gz | |||
| SRR9083560_2.fastq.gz | Fastqsanger.gz | |||
| SRR9083561_1.fastq.gz | Fastqsanger.gz | |||
| SRR9083561_2.fastq.gz | Fastqsanger.gz | |||
| SRR9083559_1.fastq.gz | Fastqsanger.gz |
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Project description:We present a tool for processing NGS DNA-seq BAM files to quantify peaks and create coverage tracks from ChIP-seq, NS-seq, ATAC-seq, OK-seq, END-seq and replication timing data
2020-05-17 | GSE131417 | GEO
Project description:BackgroundNext-generation sequencing allows genome-wide analysis of changes in chromatin states and gene expression. Data analysis of these increasingly used methods either requires multiple analysis steps, or extensive computational time. We sought to develop a tool for rapid quantification of sequencing peaks from diverse experimental sources and an efficient method to produce coverage tracks for accurate visualization that can be intuitively displayed and interpreted by experimentalists with minimal bioinformatics background. We demonstrate its strength and usability by integrating data from several types of sequencing approaches.ResultsWe have developed BAMscale, a one-step tool that processes a wide set of sequencing datasets. To demonstrate the usefulness of BAMscale, we analyzed multiple sequencing datasets from chromatin immunoprecipitation sequencing data (ChIP-seq), chromatin state change data (assay for transposase-accessible chromatin using sequencing: ATAC-seq, DNA double-strand break mapping sequencing: END-seq), DNA replication data (Okazaki fragments sequencing: OK-seq, nascent-strand sequencing: NS-seq, single-cell replication timing sequencing: scRepli-seq) and RNA-seq data. The outputs consist of raw and normalized peak scores (multiple normalizations) in text format and scaled bigWig coverage tracks that are directly accessible to data visualization programs. BAMScale also includes a visualization module facilitating direct, on-demand quantitative peak comparisons that can be used by experimentalists. Our tool can effectively analyze large sequencing datasets (~ 100 Gb size) in minutes, outperforming currently available tools.ConclusionsBAMscale accurately quantifies and normalizes identified peaks directly from BAM files, and creates coverage tracks for visualization in genome browsers. BAMScale can be implemented for a wide set of methods for calculating coverage tracks, including ChIP-seq and ATAC-seq, as well as methods that currently require specialized, separate tools for analyses, such as splice-aware RNA-seq, END-seq and OK-seq for which no dedicated software is available. BAMscale is freely available on github (https://github.com/ncbi/BAMscale).
| S-EPMC7175505 | biostudies-literature
Project description:Due to the large size, complex splicing and wide dynamic range of eukaryotic transcriptomes, RNA sequencing samples the majority of expressed genes infrequently, resulting in sparse sequencing coverage that can hinder robust isoform assembly and quantification. Targeted RNA sequencing addresses this challenge by using oligonucleotide probes to capture selected genes or regions of interest for focused sequencing. This enhanced sequencing coverage confers sensitive gene discovery, robust transcript assembly and accurate gene quantification. Here we describe a detailed protocol for all stages of targeted RNA sequencing, from initial probe design considerations, capture of targeted genes, to final assembly and quantification of captured transcripts. Initial probe design and final analysis can take less than a day, while the central experimental capture stage requires ~7 days. Targetted RNA sequencing of long noncoding RNAs
2014-03-01 | E-GEOD-52503 | biostudies-arrayexpress
Project description:Due to the large size, complex splicing and wide dynamic range of eukaryotic transcriptomes, RNA sequencing samples the majority of expressed genes infrequently, resulting in sparse sequencing coverage that can hinder robust isoform assembly and quantification. Targeted RNA sequencing addresses this challenge by using oligonucleotide probes to capture selected genes or regions of interest for focused sequencing. This enhanced sequencing coverage confers sensitive gene discovery, robust transcript assembly and accurate gene quantification. Here we describe a detailed protocol for all stages of targeted RNA sequencing, from initial probe design considerations, capture of targeted genes, to final assembly and quantification of captured transcripts. Initial probe design and final analysis can take less than a day, while the central experimental capture stage requires ~7 days.
2014-03-01 | GSE52503 | GEO
Project description:BackgroundAlthough QRS duration (QRSd) is an important determinant of cardiac resynchronization therapy (CRT) response, non-responder rates remain high. QRS fragmentation can also reflect electrical dyssynchrony. We hypothesized that quantification of abnormal QRS peaks (QRSp) would predict CRT response.MethodsForty-seven CRT patients (left ventricular ejection fraction = 23±7%) were prospectively studied. Digital 12-lead ECGs were recorded during native rhythm at baseline and 6 months post-CRT. For each precordial lead, QRSp was defined as the total number of peaks detected on the unfiltered QRS minus those detected on a smoothed moving average template QRS. CRT response was defined as >5% increase in left ventricular ejection fraction post-CRT.ResultsSixty-percent of patients responded to CRT. Baseline QRSd was similar in CRT responders and non-responders, and did not change post-CRT regardless of response. Baseline QRSp was greater in responders than non-responders (9.1±3.5 vs. 5.9±2.2, p = 0.001) and decreased in responders (9.2±3.6 vs. 7.9±2.8, p = 0.03) but increased in non-responders (5.5±2.3 vs. 7.5±2.8, p = 0.049) post-CRT. In multivariable analysis, QRSp was the only independent predictor of CRT response (Odds Ratio [95% Confidence Interval]: 1.5 [1.1-2.1], p = 0.01). ROC analysis revealed QRSp (area under curve = 0.80) to better discriminate response than QRSd (area under curve = 0.67). Compared to QRSd ≥150ms, QRSp ≥7 identified response with similar sensitivity but greater specificity (74 vs. 32%, p<0.05). Amongst patients with QRSd <150ms, more patients with QRSp ≥7 responded than those with QRSp <7 (75 vs. 0%, p<0.05).ConclusionsOur novel automated QRSp metric independently predicts CRT response and decreases in responders. Electrical dyssynchrony assessed by QRSp may improve CRT selection and track structural remodeling, especially in those with QRSd <150ms.
| S-EPMC6553860 | biostudies-literature
Project description:We report a new immunoprecipitation-coupled sequencing (DIP-Seq) application termed U-DNA-Seq, where a tailored and catalytically inactive uracil-DNA glycosylase (UNG) was used as uracil-DNA sensor to immunoprecipitate uracil containing genomic DNA fragments. Genomic uracil was profiled in drug-treated (5-fluoro-2'-deoxyuridine (5FdUR) or raltitrexed (RTX)) or non-treated (NT) HCT116 cells expressing the UNG inhibitor (UGI). The same experiments were also performed in the mismatch repair proficient version of the HCT116 cells (HCT116MMR), where chromosome 3 is reinserted to restore functional MMR (PMID: 8044777). Moreover, wild-type HCT116 or K562 cells were also measured. We found that regions of uracil enrichment in this assay were rather broad as compared to the sharp peaks typical in ChIP-seq. Therefore, we applied an approach alternative to the conventional peak calling. Namely, we calculated genome scaled coverage tracks and log2 ratio tracks of the enriched versus the input samples using deepTools package (bamCoverage and bigwigCompare tools, respectively) to provide a more appropriate description of uracil-enriched genomic regions. Interval (bed) files were also derived from these log2 ratio tracks to be able to screen large datasets for colocalizing features with them. For wider context of the study, see the related publication.
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Project description:Fibroblasts rely on adhesive structures to migrate. It is generally thought that adhesive structures disassemble once at the rear of the cell to release the cell and allow further movement. However, a significant portion of adhesions is not disassembled and is instead left on the substrate. We observed that virtually all the non-resorbed adhesions remain connected to the substrate by the means of integrin β5. Forward cell migration results in plasma membrane pulling giving rise to tubular structures that are then left on the ground, forming a network of lipid tracks that persist for several days, thus altering substrate’s topography. Cancer cells of different origin exploit fibroblast-tracks as migration railways. The adhesion of cancer cells along tracks is not mediated by focal adhesions, but rather by frustrated clathrin-coated structures (CCSs). Analysis of the composition of track generated by cancer associated fibroblasts allowed to identify integrin αv as the receptor on cancer cells responsible for track recognition. Some cancer cell lines are not able to perform durotaxis (rigidity driven migration), while fibroblasts are very good at it. We thus tested whether, in the presence of fibroblast-tracks, non-durotactic cancer cells would become durotactic by following tracks and this was indeed the case. Hence, fibroblast-tracks are a novel structure capable of physically directing cell migration of different cell types.
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