Title |
Current Challenges in the Bioinformatics of Single Cell Genomics
|
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Published in |
Frontiers in oncology, January 2014
|
DOI | 10.3389/fonc.2014.00007 |
Pubmed ID | |
Authors |
Luwen Ning, Geng Liu, Guibo Li, Yong Hou, Yin Tong, Jiankui He |
Abstract |
Single cell genomics is a rapidly growing field with many new techniques emerging in the past few years. However, few bioinformatics tools specific for single cell genomics analysis are available. Single cell DNA/RNA sequencing data usually have low genome coverage and high amplification bias, which makes bioinformatics analysis challenging. Many current bioinformatics tools developed for bulk cell sequencing do not work well with single cell sequencing data. Here, we summarize current challenges in the bioinformatics analysis of single cell genomic DNA sequencing and single cell transcriptomes. These challenges include calling copy number variations, identifying mutated genes in tumor samples, reconstructing cell lineages, recovering low abundant transcripts, and improving the accuracy of quantitative analysis of transcripts. Development in single cell genomics bioinformatics analysis will promote the application of this technology to basic biology and medical research. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 4 | 22% |
Switzerland | 2 | 11% |
United Kingdom | 1 | 6% |
Hong Kong | 1 | 6% |
France | 1 | 6% |
India | 1 | 6% |
Sao Tome and Principe | 1 | 6% |
Germany | 1 | 6% |
Unknown | 6 | 33% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 9 | 50% |
Scientists | 8 | 44% |
Science communicators (journalists, bloggers, editors) | 1 | 6% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | 1% |
Sweden | 2 | <1% |
Germany | 2 | <1% |
Belgium | 2 | <1% |
Ghana | 1 | <1% |
Italy | 1 | <1% |
Czechia | 1 | <1% |
Saudi Arabia | 1 | <1% |
Argentina | 1 | <1% |
Other | 5 | 2% |
Unknown | 221 | 92% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 58 | 24% |
Researcher | 57 | 24% |
Student > Master | 31 | 13% |
Student > Bachelor | 17 | 7% |
Other | 14 | 6% |
Other | 43 | 18% |
Unknown | 20 | 8% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 116 | 48% |
Biochemistry, Genetics and Molecular Biology | 60 | 25% |
Computer Science | 12 | 5% |
Medicine and Dentistry | 12 | 5% |
Immunology and Microbiology | 5 | 2% |
Other | 13 | 5% |
Unknown | 22 | 9% |