Application of in-house virtual protein database performed in genomic-proteomic combined research on heavy-metal stressed onion roots
(2016) In Biotechnology Letters 38(8). p.1293-1300- Abstract
- Objectives To establish an in-house virtual protein database that can be employed in proteomic research on non-model plants. Results A total of 87,430 unigenes were obtained through transcriptome sequencing from onion roots. Of these, 24,305 unigenes were annotated and their nucleotide sequences of coding regions were translated into amino acid sequences. The corresponding 24,305 amino acid sequences were considered as an in-house virtual protein database. Thirty-two protein spots with significant differential abundance were selected. Their MS data were submitted to a restriction enzyme map which was converted from the in-house virtual protein database. A total of 27 proteins were finally matched.
Conclusions The in-house protein... (More) - Objectives To establish an in-house virtual protein database that can be employed in proteomic research on non-model plants. Results A total of 87,430 unigenes were obtained through transcriptome sequencing from onion roots. Of these, 24,305 unigenes were annotated and their nucleotide sequences of coding regions were translated into amino acid sequences. The corresponding 24,305 amino acid sequences were considered as an in-house virtual protein database. Thirty-two protein spots with significant differential abundance were selected. Their MS data were submitted to a restriction enzyme map which was converted from the in-house virtual protein database. A total of 27 proteins were finally matched.
Conclusions The in-house protein database is a
feasible and innovative strategy for proteomic
research on non-model plants. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3ed6a6b6-e29c-47a3-b92b-4b2dbf0ccdc3
- author
- Ning, Chanjuan ; Qin, Rong ; Chen, Da ; Björn, Lars Olof LU and Li, Shaoshan
- organization
- publishing date
- 2016
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- In-house database, Non-model plants, Proteomic research, Restriction enzyme map, Unigenes
- in
- Biotechnology Letters
- volume
- 38
- issue
- 8
- pages
- 8 pages
- publisher
- Springer
- external identifiers
-
- scopus:84966270821
- pmid:27154469
- wos:000379748600007
- ISSN
- 1573-6776
- DOI
- 10.1007/s10529-016-2114-3
- language
- English
- LU publication?
- yes
- id
- 3ed6a6b6-e29c-47a3-b92b-4b2dbf0ccdc3
- date added to LUP
- 2016-06-06 17:11:55
- date last changed
- 2023-08-21 15:19:32
@article{3ed6a6b6-e29c-47a3-b92b-4b2dbf0ccdc3, abstract = {{Objectives To establish an in-house virtual protein database that can be employed in proteomic research on non-model plants. Results A total of 87,430 unigenes were obtained through transcriptome sequencing from onion roots. Of these, 24,305 unigenes were annotated and their nucleotide sequences of coding regions were translated into amino acid sequences. The corresponding 24,305 amino acid sequences were considered as an in-house virtual protein database. Thirty-two protein spots with significant differential abundance were selected. Their MS data were submitted to a restriction enzyme map which was converted from the in-house virtual protein database. A total of 27 proteins were finally matched.<br/>Conclusions The in-house protein database is a<br/>feasible and innovative strategy for proteomic<br/>research on non-model plants.}}, author = {{Ning, Chanjuan and Qin, Rong and Chen, Da and Björn, Lars Olof and Li, Shaoshan}}, issn = {{1573-6776}}, keywords = {{In-house database; Non-model plants; Proteomic research; Restriction enzyme map; Unigenes}}, language = {{eng}}, number = {{8}}, pages = {{1293--1300}}, publisher = {{Springer}}, series = {{Biotechnology Letters}}, title = {{Application of in-house virtual protein database performed in genomic-proteomic combined research on heavy-metal stressed onion roots}}, url = {{http://dx.doi.org/10.1007/s10529-016-2114-3}}, doi = {{10.1007/s10529-016-2114-3}}, volume = {{38}}, year = {{2016}}, }