Assessing the reproducibility of discriminant function analyses.

Data are the foundation of empirical research, yet all too often the datasets underlying published papers are unavailable, incorrect, or poorly curated. This is a serious issue, because future researchers are then unable to validate published results or reuse data to explore new ideas and hypotheses. Even if data files are securely stored and accessible, they must also be accompanied by accurate labels and identifiers. To assess how often problems with metadata or data curation affect the reproducibility of published results, we attempted to reproduce Discriminant Function Analyses (DFAs) from the field of organismal biology. DFA is a commonly used statistical analysis that has changed little since its inception almost eight decades ago, and therefore provides an opportunity to test reproducibility among datasets of varying ages. Out of 100 papers we initially surveyed, fourteen were excluded because they did not present the common types of quantitative result from their DFA or gave insufficient details of their DFA. Of the remaining 86 datasets, there were 15 cases for which we were unable to confidently relate the dataset we received to the one used in the published analysis. The reasons ranged from incomprehensible or absent variable labels, the DFA being performed on an unspecified subset of the data, or the dataset we received being incomplete. We focused on reproducing three common summary statistics from DFAs: the percent variance explained, the percentage correctly assigned and the largest discriminant function coefficient. The reproducibility of the first two was fairly high (20 of 26, and 44 of 60 datasets, respectively), whereas our success rate with the discriminant function coefficients was lower (15 of 26 datasets). When considering all three summary statistics, we were able to completely reproduce 46 (65%) of 71 datasets. While our results show that a majority of studies are reproducible, they highlight the fact that many studies still are not the carefully curated research that the scientific community and public expects

Toxin Induction and Protein Extraction from Fusariumspp. Cultures for Proteomic Studies

Fusaria are filamentous fungi able to produce different toxins. Fusarium mycotoxins such as deoxynivalenol, nivalenol, T2, zearelenone, fusaric acid, moniliformin, etc... have adverse effects on both human and animal health and some are considered as pathogenicity factors. Proteomic studies showed to be effective for deciphering toxin production mechanisms (Taylor et al., 2008) as well as for identifying potential pathogenic factors (Paper et al., 2007, Houterman et al., 2007) in Fusaria. It becomes therefore fundamental to establish reliable methods for comparing between proteomic studies in order to rely on true differences found in protein expression among experiments, strains and laboratories. The procedure that will be described should contribute to an increased level of standardization of proteomic procedures by two ways. The filmed protocol is used to increase the level of details that can be described precisely. Moreover, the availability of standardized procedures to process biological replicates should guarantee a higher robustness of data, taking into account also the human factor within the technical reproducibility of the extraction procedure

Assessing the reproducibility of discriminant function analyses

Data are the foundation of empirical research, yet all too often the datasets underlying published papers are unavailable, incorrect, or poorly curated. This is a serious issue, because future researchers are then unable to validate published results or reuse data to explore new ideas and hypotheses. Even if data files are securely stored and accessible, they must also be accompanied by accurate labels and identifiers. To assess how often problems with metadata or data curation affect the reproducibility of published results, we attempted to reproduce Discriminant Function Analyses (DFAs) from the field of organismal biology. DFA is a commonly used statistical analysis that has changed little since its inception almost eight decades ago, and therefore provides an opportunity to test reproducibility among datasets of varying ages. Out of 100 papers we initially surveyed, fourteen were excluded because they did not present the common types of quantitative result from their DFA or gave insufficient details of their DFA. Of the remaining 86 datasets, there were 15 cases for which we were unable to confidently relate the dataset we received to the one used in the published analysis. The reasons ranged from incomprehensible or absent variable labels, the DFA being performed on an unspecified subset of the data, or the dataset we received being incomplete. We focused on reproducing three common summary statistics from DFAs: the percent variance explained, the percentage correctly assigned and the largest discriminant function coefficient. The reproducibility of the first two was fairly high (20 of 26, and 44 of 60 datasets, respectively), whereas our success rate with the discriminant function coefficients was lower (15 of 26 datasets). When considering all three summary statistics, we were able to completely reproduce 46 (65%) of 71 datasets. While our results show that a majority of studies are reproducible, they highlight the fact that many studies still are not the carefully curated research that the scientific community and public expects

Primary metabolism is distinctly modulated by plant resistance inducers in Coffea arabica leaves infected by Hemileia vastattrix

Original ResearchEpidemics of coffee leaf rust (CLR) leads to great yield losses and huge depreciation of coffee marketing values, if no control measures are applied. Societal expectations of a more sustainable coffee production are increasingly imposing the replacement of fungicide treatments by alternative solutions. A protection strategy is to take advantage of the plant immune system by eliciting constitutive defenses. Based on such concept, plant resistance inducers (PRIs) have been developed. The Greenforce CuCa formulation, similarly to acibenzolar-S-methyl (ASM), shows promising results in the control of CLR (Hemileia vastatrix) in Coffea arabica cv. Mundo Novo. The molecular mechanisms of PRIs action are poorly understood. In order to contribute to its elucidation a proteomic, physiological (leaf gas-exchange) and biochemical (enzymatic) analyses were performed. Coffee leaves treated with Greenforce CuCa and ASM and inoculation with H. vastatrix were considered. Proteomics revealed that both PRIs lead to metabolic adjustments but, inducing distinct proteins. These proteins were related with photosynthesis, protein metabolism and stress responses. Greenforce CuCa increased photosynthesis and stomatal conductance, while ASM caused a decrease in these parameters. It was further observed that Greenforce CuCa reinforces the redox homeostasis of the leaf, while ASM seems to affect preferentially the secondary metabolism and the stress-related proteins. So, the PRIs prepare the plant to resist CLR but, inducing different defense mechanisms upon pathogen infection. The existence of a link between the primary metabolism and defense responses was evidenced. The identification of components of the plant primary metabolism, essential for plant growth and development that, simultaneously, participate in the plant defense responses can open new perspectives for plant breeding programsinfo:eu-repo/semantics/publishedVersio

ordered_transcriptome_trinity.txt

This is the genetic map used to position genes and SNPs. Column 1 refers to the contig name, column 2 to the chromosome number and column 3 to the position in centimorgan

Data from: The accumulation of deleterious mutations as a consequence of domestication and improvement in sunflowers and other Compositae crops

For populations to maintain optimal fitness, harmful mutations must be efficiently purged from the genome. Yet, under circumstances that diminish the effectiveness of natural selection, such as the process of plant and animal domestication, deleterious mutations are predicted to accumulate. Here, we compared the load of deleterious mutations in 21 accessions from natural populations and 19 domesticated accessions of the common sunflower using whole-transcriptome single nucleotide polymorphism data. Although we find that genetic diversity has been greatly reduced during domestication, the remaining mutations were disproportionally biased toward nonsynonymous substitutions. Bioinformatically predicted deleterious mutations affecting protein function were especially strongly over-represented. We also identify similar patterns in two other domesticated species of the sunflower family (globe artichoke and cardoon), indicating that this phenomenon is not due to idiosyncrasies of sunflower domestication or the sunflower genome. Finally, we provide unequivocal evidence that deleterious mutations accumulate in low recombining regions of the genome, due to the reduced efficacy of purifying selection. These results represent a conundrum for crop improvement efforts. Although the elimination of harmful mutations should be a long-term goal of plant and animal breeding programs, it will be difficult to weed them out because of limited recombination

table_S1_24_02_15

Table S1 with information about samples (location, sequencing stats, etc.

new_sift_results_all

Results of SIFT analyses identifying deleterious non-synonymous mutations. First column identifies the non-synonymous AA changes and their positions (e.g. S11T). Column 2-6 are the SIFT statistics. Last column are the name of the genes

HA412_trinity_noAltSplice_400bpmin.fa

Link to reference transcriptome described in Renaut et al. 2013 (NatCom), used for all alignments, and previously deposited in Dryad as part of http://dx.doi.org/10.5061/dryad.9q1n4

unique_orf

Unique (longest) open reading frames identified in the reference transcriptom