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X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
Bioinformatics for Novel Long Intergenic Noncoding RNA (lincRNA) Identification in Skeletal Muscle Cells
|
|---|---|
| Chapter number | 20 |
| Book title |
Muscle Stem Cells
|
| Published in |
Methods in molecular biology, March 2017
|
| DOI | 10.1007/978-1-4939-6771-1_20 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-6769-8, 978-1-4939-6771-1
|
| Authors |
Xianlu Peng, Kun Sun, Jiajian Zhou, Hao Sun, Huating Wang, Peng, Xianlu, Sun, Kun, Zhou, Jiajian, Sun, Hao, Wang, Huating |
| Editors |
Eusebio Perdiguero, DDW Cornelison |
| Abstract |
Long intergenic noncoding RNAs (lincRNAs) have emerged as critical participators in gene regulation in myriads of cell types. The development of the whole transcriptome sequencing technology, or RNA-seq , has enabled novel lincRNA detection, but the bioinformatics analysis toward distinguishing reliable ones remains a challenge. Here, we describe the bioinformatics workflow developed for identifying novel lincRNAs step by step, including read alignment, transcriptome assembly and transcript filtering. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Greece | 1 | 8% |
| Unknown | 12 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 3 | 23% |
| Other | 2 | 15% |
| Student > Ph. D. Student | 2 | 15% |
| Student > Doctoral Student | 1 | 8% |
| Student > Bachelor | 1 | 8% |
| Other | 4 | 31% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 5 | 38% |
| Biochemistry, Genetics and Molecular Biology | 4 | 31% |
| Computer Science | 2 | 15% |
| Immunology and Microbiology | 1 | 8% |
| Neuroscience | 1 | 8% |
| Other | 0 | 0% |
Attention Score in Context
This research output has an Altmetric Attention Score of 14. 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 March 2017.
All research outputs
#2,279,721
of 22,958,253 outputs
Outputs from Methods in molecular biology
#400
of 13,137 outputs
Outputs of similar age
#46,017
of 311,244 outputs
Outputs of similar age from Methods in molecular biology
#3
of 268 outputs
Altmetric has tracked 22,958,253 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 13,137 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done particularly well, scoring higher than 96% 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 311,244 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 85% of its contemporaries.
We're also able to compare this research output to 268 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 98% of its contemporaries.