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X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
Computational Analysis of LncRNA from cDNA Sequences.
|
|---|---|
| Chapter number | 20 |
| Book title |
Long Non-Coding RNAs
|
| Published in |
Methods in molecular biology, January 2016
|
| DOI | 10.1007/978-1-4939-3378-5_20 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-3376-1, 978-1-4939-3378-5
|
| Authors |
Boerner, Susan, McGinnis, Karen M, Susan Boerner, Karen M. McGinnis |
| Editors |
Yi Feng, Lin Zhang |
| Abstract |
Based on recent findings, long noncoding (lnc) RNAs represent a potential class of functional molecules within the cell. In this chapter we describe a computational scheme to identify and classify lncRNAs within maize from full-length cDNA sequences to designate subsets of lncRNAs for which biogenesis and regulatory mechanisms may be verified at the bench. We make use of the Coding Potential Calculator and specific Python scripts in our approach. |
X Demographics
The data shown below were collected from the profiles of 3 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 | 1 | 33% |
| United States | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 5 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 3 | 60% |
| Student > Ph. D. Student | 2 | 40% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 2 | 40% |
| Agricultural and Biological Sciences | 2 | 40% |
| Biochemistry, Genetics and Molecular Biology | 1 | 20% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 03 January 2016.
All research outputs
#15,352,477
of 22,836,570 outputs
Outputs from Methods in molecular biology
#5,347
of 13,126 outputs
Outputs of similar age
#230,873
of 393,564 outputs
Outputs of similar age from Methods in molecular biology
#545
of 1,470 outputs
Altmetric has tracked 22,836,570 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,126 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 44th percentile – i.e., 44% of its peers scored the same or lower than it.
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We're also able to compare this research output to 1,470 others from the same source and published within six weeks on either side of this one. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.