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X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
High Throughput Sequencing-Based Approaches for Gene Expression Analysis
|
|---|---|
| Chapter number | 15 |
| Book title |
Gene Expression Analysis
|
| Published in |
Methods in molecular biology, January 2018
|
| DOI | 10.1007/978-1-4939-7834-2_15 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-7833-5, 978-1-4939-7834-2
|
| Authors |
R. Raja Sekhara Reddy, M. V. Ramanujam |
| Abstract |
Next-generation sequencing has emerged as the method of choice to answer fundamental questions in biology. The massively parallel sequencing technology for RNA-Seq analysis enables better understanding of gene expression patterns in model and nonmodel organisms. Sequencing per se has reached the stage of commodity level while analyzing and interpreting huge amount of data has been a significant challenge. This chapter is aimed at discussing the complexities involved in sequencing and analysis, and tries to simplify sequencing based gene expression analysis. Biologists and experimental scientists were kept in mind while discussing the methods and analysis workflow. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 14 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 3 | 21% |
| Other | 2 | 14% |
| Student > Bachelor | 2 | 14% |
| Student > Ph. D. Student | 1 | 7% |
| Researcher | 1 | 7% |
| Other | 1 | 7% |
| Unknown | 4 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 5 | 36% |
| Agricultural and Biological Sciences | 1 | 7% |
| Computer Science | 1 | 7% |
| Immunology and Microbiology | 1 | 7% |
| Medicine and Dentistry | 1 | 7% |
| Other | 0 | 0% |
| Unknown | 5 | 36% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 17 May 2018.
All research outputs
#14,988,291
of 23,056,273 outputs
Outputs from Methods in molecular biology
#4,747
of 13,196 outputs
Outputs of similar age
#255,896
of 442,477 outputs
Outputs of similar age from Methods in molecular biology
#509
of 1,499 outputs
Altmetric has tracked 23,056,273 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,196 research outputs from this source. They receive a mean Attention Score of 3.4. This one has gotten more attention than average, scoring higher than 59% 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 442,477 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,499 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 60% of its contemporaries.