It is well known that significant metabolic change take place as cells are transformed from normal to malignant. This review focuses on the use of different bioinformatics tools in cancer metabolomics studies. The article begins by describing different metabolomics technologies and data generation techniques. Overview of the data pre-processing techniques is provided and multivariate data analysis techniques are discussed and illustrated with case studies, including principal component analysis, clustering techniques, self-organizing maps, partial least squares, and discriminant function analysis. Also included is a discussion of available software packages.

Manual phenotyping for papaya Carica papaya (L) breeding purposes limits the evaluation of a great number of plants and hampers selection of superior genotypes. This study aimed to validate two methodologies for the phenotyping of morpho-agronomic plant traits using image analysis and fruit traits through image processing. In plants of the ‘THB’ variety and ‘UENF/Caliman-01’ hybrid two images (A and B) were analyzed to estimate commercial and irregularly shaped fruits. Image A was also used in the estimation of plant height, stem diameter and the first fruit insertion height. In ‘THB’ fruits, largest and smallest diameters, length, and volume were estimated by using a caliper and image processing (IP). Volume was obtained by water column displacement (WCD) and by the expression of ellipsoid approximation (EA). Correlations above 0.85 between manual and image measurements were obtained for all traits. The averages of the morpho-agronomic traits, estimated by using images, were similar when compared to the averages measured manually. In addition, the errors of the proposed methodologies were low compared to manual phenotyping. Bland-Altman's approach indicated agreement between the volume estimated by WCD and EA using caliper and IP. The strong association obtained between volume and fruit weight suggests the use of regression to estimate this trait. Thus, the expectation is that image-based phenotyping can be used to expand the experiments, thereby maintaining accuracy and providing greater genetic gains in the selection of superior genotypes.

Cassava (Manihot esculenta Crantz) is an important food security crop in many parts of the developing world. The crop’s high yield potential and multitude of uses–both for nutrition and processing–render cassava a promising driver for the development of rural value chains. It is traditionally propagated from stem cuttings of up to 30 cm in length, giving a multiplication rate as low as 1:10. Propagating cassava traditionally is very inefficient, which leads to challenges in the production and distribution of quality planting material and improved cultivars, greatly limiting the impact of investments in crop breeding. The work described in the present study aimed to develop a seed treatment approach to facilitate the use of shorter seed pieces, increasing the multiplication rate of cassava and thus making the crop’s seed systems more efficient. After several tests, formulation was identified, consisting of thiamethoxam 21 g ha^-1, mefenoxam 1.0 g ha^-1, fludioxonil 1.3 g ha^-1, thiabendazole 7.5 g ha^-1 and Latex 2% as a binder. Plant growing from seed pieces treated with this formulation displayed increased crop establishment and early crop vigor, leading to an improved productivity throughout a full growing cycle. This allowed to reduce the cassava seed piece size to 8 cm with no negative effects on germination and crop establishment, leading to yields comparable to those from untreated 16 cm pieces. This, in turn, will allow to increase the multiplication ratio of cassava by a factor of up to 3. Notably, this was possible under regular field conditions and independently of any specialised treatment facilities. Compared with existing seed production protocols, the increased multiplication rates allowed for efficiency gains of between 1 to 1.9 years compared to conventional five-year cycles. We believe that the technology described here holds considerable promise for developing more reliable and remunerative delivery channels for quality cassava planting material and improved genetics.