| Title |
Annotation of the Zebrafish Genome through an Integrated Transcriptomic and Proteomic Analysis
|
|---|---|
| Published in |
Molecular and Cellular Proteomics, July 2014
|
| DOI | 10.1074/mcp.m114.038299 |
| Pubmed ID | |
| Authors |
Dhanashree S Kelkar, Elayne Provost, Raghothama Chaerkady, Babylakshmi Muthusamy, Srikanth S Manda, Tejaswini Subbannayya, Lakshmi Dhevi N Selvan, Chieh-Huei Wang, Keshava K Datta, Sunghee Woo, Sutopa B Dwivedi, Santosh Renuse, Derese Getnet, Tai-Chung Huang, Min-Sik Kim, Sneha M Pinto, Christopher J Mitchell, Anil K Madugundu, Praveen Kumar, Jyoti Sharma, Jayshree Advani, Gourav Dey, Lavanya Balakrishnan, Nazia Syed, Vishalakshi Nanjappa, Yashwanth Subbannayya, Renu Goel, T S Keshava Prasad, Vineet Bafna, Ravi Sirdeshmukh, Harsha Gowda, Charles Wang, Steven D Leach, Akhilesh Pandey |
| Abstract |
Accurate annotation of protein-coding genes is one of the primary tasks upon completion of whole genome sequencing of any organism. In this study, we used an integrated transcriptomic and proteomic strategy to validate and improve the existing zebrafish genome annotation. We undertook high resolution mass spectrometry-based proteomic profiling of ten adult organs, whole adult fish body and two developmental stages of zebrafish (SAT line) in addition to transcriptomic profiling of six organs. More than 7,000 proteins were identified from proteomic analyses and ~69,000 high confidence transcripts were assembled from the RNA-Seq data. Approximately 15% of the transcripts mapped to intergenic regions, the majority of which are likely long non-coding RNAs. These high quality transcriptomic and proteomic data were used to manually re-annotate the zebrafish genome. We report identification of 157 novel protein-coding genes. In addition, our data led to modification of existing gene structures which include novel exons, change in exon coordinates, change in frame of translation, translation in annotated UTRs and joining of genes. Finally, we discovered four instances of genome assembly errors that were supported by both proteomic and transcriptomic data. Our study shows how an integrative analysis of the transcriptome and the proteome can extend our understanding of even well-annotated genomes. |
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X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Scientists | 1 | 50% |
Mendeley demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Uruguay | 1 | 1% |
| United States | 1 | 1% |
| Germany | 1 | 1% |
| Unknown | 94 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 19 | 20% |
| Student > Ph. D. Student | 17 | 18% |
| Student > Master | 15 | 15% |
| Student > Bachelor | 7 | 7% |
| Other | 6 | 6% |
| Other | 13 | 13% |
| Unknown | 20 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 30 | 31% |
| Agricultural and Biological Sciences | 20 | 21% |
| Computer Science | 10 | 10% |
| Medicine and Dentistry | 4 | 4% |
| Engineering | 3 | 3% |
| Other | 8 | 8% |
| Unknown | 22 | 23% |