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X Demographics
Mendeley readers
Attention Score in Context
| Title |
methylPipe and compEpiTools: a suite of R packages for the integrative analysis of epigenomics data
|
|---|---|
| Published in |
BMC Bioinformatics, September 2015
|
| DOI | 10.1186/s12859-015-0742-6 |
| Pubmed ID | |
| Authors |
Kamal Kishore, Stefano de Pretis, Ryan Lister, Marco J. Morelli, Valerio Bianchi, Bruno Amati, Joseph R. Ecker, Mattia Pelizzola |
| Abstract |
Numerous methods are available to profile several epigenetic marks, providing data with different genome coverage and resolution. Large epigenomic datasets are then generated, and often combined with other high-throughput data, including RNA-seq, ChIP-seq for transcription factors (TFs) binding and DNase-seq experiments. Despite the numerous computational tools covering specific steps in the analysis of large-scale epigenomics data, comprehensive software solutions for their integrative analysis are still missing. Multiple tools must be identified and combined to jointly analyze histone marks, TFs binding and other -omics data together with DNA methylation data, complicating the analysis of these data and their integration with publicly available datasets. To overcome the burden of integrating various data types with multiple tools, we developed two companion R/Bioconductor packages. The former, methylPipe, is tailored to the analysis of high- or low-resolution DNA methylomes in several species, accommodating (hydroxy-)methyl-cytosines in both CpG and non-CpG sequence context. The analysis of multiple whole-genome bisulfite sequencing experiments is supported, while maintaining the ability of integrating targeted genomic data. The latter, compEpiTools, seamlessly incorporates the results obtained with methylPipe and supports their integration with other epigenomics data. It provides a number of methods to score these data in regions of interest, leading to the identification of enhancers, lncRNAs, and RNAPII stalling/elongation dynamics. Moreover, it allows a fast and comprehensive annotation of the resulting genomic regions, and the association of the corresponding genes with non-redundant GeneOntology terms. Finally, the package includes a flexible method based on heatmaps for the integration of various data types, combining annotation tracks with continuous or categorical data tracks. methylPipe and compEpiTools provide a comprehensive Bioconductor-compliant solution for the integrative analysis of heterogeneous epigenomics data. These packages are instrumental in providing biologists with minimal R skills a complete toolkit facilitating the analysis of their own data, or in accelerating the analyses performed by more experienced bioinformaticians. |
X Demographics
The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 2 | 29% |
| Italy | 1 | 14% |
| United Kingdom | 1 | 14% |
| Unknown | 3 | 43% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 4 | 57% |
| Members of the public | 3 | 43% |
Mendeley readers
The data shown below were compiled from readership statistics for 158 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 1% |
| Spain | 2 | 1% |
| Switzerland | 1 | <1% |
| Italy | 1 | <1% |
| Chile | 1 | <1% |
| Norway | 1 | <1% |
| United Kingdom | 1 | <1% |
| Unknown | 149 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 54 | 34% |
| Researcher | 33 | 21% |
| Student > Master | 19 | 12% |
| Student > Doctoral Student | 9 | 6% |
| Professor | 8 | 5% |
| Other | 23 | 15% |
| Unknown | 12 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 71 | 45% |
| Biochemistry, Genetics and Molecular Biology | 36 | 23% |
| Computer Science | 9 | 6% |
| Engineering | 6 | 4% |
| Medicine and Dentistry | 5 | 3% |
| Other | 10 | 6% |
| Unknown | 21 | 13% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 October 2015.
All research outputs
#7,467,636
of 22,829,083 outputs
Outputs from BMC Bioinformatics
#3,024
of 7,287 outputs
Outputs of similar age
#92,769
of 274,379 outputs
Outputs of similar age from BMC Bioinformatics
#56
of 141 outputs
Altmetric has tracked 22,829,083 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,287 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 50% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 274,379 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.
We're also able to compare this research output to 141 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 58% of its contemporaries.