Dataset Information

Shotgun proteomics of Ophrys orchid flowers

ABSTRACT: Proteomics data from a combind transcriptome/proteome study of three sexually deceptive orchids of the genus Ophrys. Data are from labella of mature, unpollinated flowers of (1) Ophrys exaltata subsp. archipelagi, (2) O. sphegodes, and (3) O. garganica. Proteomics data were searched against SwissProt and TAIR databases and further against organism-specific databases obtained from transcriptome sequencing (454, Sanger ESTs and Solexa data). Thirteen trypsinised gel slices per sample were subjected to electrospray ionisation-based LC-MS/MS analysis with a 2D linear ion trap Finnigan LTQ (Thermo Electron Corporation) equipped with an Ultimate Nano HPLC System (Dionex Corporation). Mass spectra were searched against SwissProt and Arabidopsis TAIR9 protein databases to identify peptides. Additionally, spectra were searched against protein databases created from the Ophrys reference transcriptome obtained in this study. Stringent criteria were used for the assignment of spectra to peptides (95% peptide identification probability) in Scaffold 3.3 (Proteome Software Inc., USA). In order to maximise the utility of proteomics data for uncovering proteins predicted by the orchid transcriptome, a minimum of one unique peptide was used for protein identification, while using two different stringency levels for the probabilistic assignment of peptides to proteins (99% for highest quality, HQ; 90% to maximise protein discovery, PD, in the absence of a fully sequenced genome). Concerning the sequencing and transcriptomics results: Three normalised cDNA libraries were constructed from three different Ophrys species, O. exaltata, O. garganica, and O. sphegodes. These libraries were 454 pyrosequenced and all the high quality reads generated in this study are available in the Sequence Read Archive (SRA) of the National Centre for Biotechnology Information (NCBI) with the accession number SRA060767. Additional sequencing of O. sphegodes flower labella yielded 1.7 Mbp of Sanger (dbEST library LIBEST_028084; dbEST IDs 77978749-77979571; GenBank accessions JZ163765-JZ164587) and 2.5 Gbp of Illumina Solexa (SRA060767) data.

INSTRUMENT(S): LTQ

ORGANISM(S): Ophrys Exaltata Subsp. Archipelagi (orchid) (ophrys Arachnitiformis Subsp. Archipelagi) Ophrys Garganica Ophrys Sphegodes (early Spider Orchid) (arachnites Aranifera)

TISSUE(S): Flower

SUBMITTER: Philipp Schlueter

LAB HEAD: Philipp Schlueter

PROVIDER: PXD000069 | Pride | 2013-06-21

REPOSITORIES: pride

ACCESS DATA

Dataset's files

Source:

Items per page:

1 - 5 of 859

Publications

Transcriptome and proteome data reveal candidate genes for pollinator attraction in sexually deceptive orchids.

Sedeek Khalid E M KE Qi Weihong W Schauer Monica A MA Gupta Alok K AK Poveda Lucy L Xu Shuqing S Liu Zhong-Jian ZJ Grossniklaus Ueli U Schiestl Florian P FP Schlüter Philipp M PM

PloS one 20130529 5

<h4>Background</h4>Sexually deceptive orchids of the genus Ophrys mimic the mating signals of their pollinator females to attract males as pollinators. This mode of pollination is highly specific and leads to strong reproductive isolation between species. This study aims to identify candidate genes responsible for pollinator attraction and reproductive isolation between three closely related species, O. exaltata, O. sphegodes and O. garganica. Floral traits such as odour, colour and morphology a ...[more]

PMID: 23734209

Similar Datasets

Project description:Protein identification was carried out by searching against a joint Human + Yeast Swissprot database (Uniprot release 57.3 May 2009, 26885 entries), in order to increase statistical power of peptide identification, which was supplemented with porcine trypsin, and using SEQUEST algorithm (Bioworks 3.2 package, Thermo Finnigan). For SILAC, we used variable (methionine oxidation, lysine and arginine modification of +6 Da) and fixed modifications (cysteine carboxamidomethylation). Precursor mass tolerance was set to 2Da, fragment mass tolerance to 1.2Da and up to two missed cleavage sites for trypsin were allowed. The same collections of MS/MS spectra were also searched against inverted databases constructed from the same target databases. SEQUEST results were analyzed using the probability ratio method 22b, taking into account isoelectric points of peptides to improve peptide identification 9b. False discovery rates (FDR) of peptide identifications were calculated from the search results against the inverted databases using the refined method 9b. The combination of stable isotope labeling (SIL) with mass spectrometry (MS) allows comparison of the abundance of thousands of proteins in complex mixtures. However, interpretation of the large datasets generated by these techniques remains a challenge since appropriate statistical standards are lacking. Here we present the first generally applicable model that accurately explains the behavior of data obtained using current SIL approaches, including 18O, iTRAQ and SILAC labeling, and different MS instruments. The model decomposes the total technical variance into the spectral, peptide and protein variance components, and its general validity was demonstrated by confronting 48 experimental distributions against 18 different null hypotheses. In addition to its general applicability, the performance of the algorithm was similar or better than that of other existing methods. The model also provides a general framework to integrate quantitative information, allowing a comparative analysis of the results obtained from different SIL experiments. The model was applied to the global analysis of protein alterations induced by low H2O2 concentrations in yeast, demonstrating the increased statistical power that may be achieved by rigorous data integration. Our results highlight the importance of establishing an adequate and validated statistical framework for the analysis of high-throughput data. Except for TOF/TOF MS/MS files, protein identification was carried out by searching against a joint Human+YeastSwissprot database (Uniprot release 57.3 May 2009, 26885 entries), in order to increase statistical power of peptide identification, which was supplemented with porcine trypsin, and using SEQUEST algorithm (Bioworks 3.2 package, Thermo Finnigan). For 18O-labeled samples variable (methionine oxidation, lysine and arginine modification of +4 Da) and fixed modifications (cysteine carboxamidomethylation) were used. For iTRAQ, we allowed variable (methionine oxidation) and fixed modifications (cysteine carboxamidomethylation, lysine and N- terminal modification of +144.1020 Da). For SILAC, we used variable (methionine oxidation, lysine and arginine modification of +6 Da) and fixed modifications (cysteine carboxamidomethylation). Precursor mass tolerance was set to 2Da, fragment mass tolerance to 1.2Da and up to two missed cleavage sites for trypsin were allowed. The same collections of MS/MS spectra were also searched against inverted databases constructed from the same target databases. SEQUEST results were analyzed using the probability ratio method 22b, taking into account isoelectric points of peptides to improve peptide identification 9b. False discovery rates (FDR) of peptide identifications were calculated from the search results against the inverted databases using the refined method 9b. TOF/TOF MS/MS spectra were converted to MGF files by using the 4000 Series Explorer V.3.5.1 internal processor and the following parameters: mass range from precursor -20 Da to 60 Da, peak density of 50 peaks per 200 Da with S/N >1 and minimum area >1 with a maximum number of 100 peaks per precursor. These files were analyzed with the Mascot V2.2.06 search engine using the database and search conditions indicated above.

			Action	DRS
	20100909_46_E0.RAW	Raw
	20100909_46_E0.mgf	Mgf
	20100909_47_E1.RAW	Raw
	20100909_47_E1.mgf	Mgf
	20100909_48_E2.RAW	Raw