| Title |
The Landscape of long noncoding RNA classification
|
|---|---|
| Published in |
Trends in Genetics, April 2015
|
| DOI | 10.1016/j.tig.2015.03.007 |
| Pubmed ID | |
| Authors | |
| Abstract |
Advances in the depth and quality of transcriptome sequencing have revealed many new classes of long noncoding RNAs (lncRNAs). lncRNA classification has mushroomed to accommodate these new findings, even though the real dimensions and complexity of the noncoding transcriptome remain unknown. Although evidence of functionality of specific lncRNAs continues to accumulate, conflicting, confusing, and overlapping terminology has fostered ambiguity and lack of clarity in the field in general. The lack of fundamental conceptual unambiguous classification framework results in a number of challenges in the annotation and interpretation of noncoding transcriptome data. It also might undermine integration of the new genomic methods and datasets in an effort to unravel the function of lncRNA. Here, we review existing lncRNA classifications, nomenclature, and terminology. Then, we describe the conceptual guidelines that have emerged for their classification and functional annotation based on expanding and more comprehensive use of large systems biology-based datasets. |
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X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 6 | 38% |
| United Kingdom | 3 | 19% |
| France | 1 | 6% |
| India | 1 | 6% |
| Unknown | 5 | 31% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 13 | 81% |
| Members of the public | 3 | 19% |
Mendeley demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 5 | <1% |
| United Kingdom | 3 | <1% |
| Spain | 3 | <1% |
| Poland | 2 | <1% |
| Colombia | 2 | <1% |
| Turkey | 1 | <1% |
| Mexico | 1 | <1% |
| Korea, Republic of | 1 | <1% |
| Japan | 1 | <1% |
| Other | 6 | <1% |
| Unknown | 965 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 222 | 22% |
| Researcher | 145 | 15% |
| Student > Master | 133 | 13% |
| Student > Bachelor | 78 | 8% |
| Student > Doctoral Student | 44 | 4% |
| Other | 156 | 16% |
| Unknown | 212 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 309 | 31% |
| Agricultural and Biological Sciences | 266 | 27% |
| Medicine and Dentistry | 57 | 6% |
| Immunology and Microbiology | 22 | 2% |
| Neuroscience | 21 | 2% |
| Other | 75 | 8% |
| Unknown | 240 | 24% |