OmicLoupe: facilitating biological discovery by interactive exploration of multiple omic datasets and statistical comparisons.

BACKGROUND: Visual exploration of gene product behavior across multiple omic datasets can pinpoint technical limitations in data and reveal biological trends. Still, such exploration is challenging as there is a need for visualizations that are tailored for the purpose. RESULTS: The OmicLoupe software was developed to facilitate visual data exploration and provides more than 15 interactive cross-dataset visualizations for omics data. It expands visualizations to multiple datasets for quality control, statistical comparisons and overlap and correlation analyses, while allowing for rapid inspection and downloading of selected features. The usage of OmicLoupe is demonstrated in three different studies, where it allowed for detection of both technical data limitations and biological trends across different omic layers. An example is an analysis of SARS-CoV-2 infection based on two previously published studies, where OmicLoupe facilitated the identification of gene products with consistent expression changes across datasets at both the transcript and protein levels. CONCLUSIONS: OmicLoupe provides fast exploration of omics data with tailored visualizations for comparisons within and across data layers. The interactive visualizations are highly informative and are expected to be useful in various analyses of both newly generated and previously published data. OmicLoupe is available at quantitativeproteomics.org/omicloupe.

NormalyzerDE: Online Tool for Improved Normalization of Omics Expression Data and High-Sensitivity Differential Expression Analysis.

Technical biases are introduced in omics data sets during data generation and interfere with the ability to study biological mechanisms. Several normalization approaches have been proposed to minimize the effects of such biases, but fluctuations in the electrospray current during liquid chromatography-mass spectrometry gradients cause local and sample-specific bias not considered by most approaches. Here we introduce a software named NormalyzerDE that includes a generic retention time (RT)-segmented approach compatible with a wide range of global normalization approaches to reduce the effects of time-resolved bias. The software offers straightforward access to multiple normalization methods, allows for data set evaluation and normalization quality assessment as well as subsequent or independent differential expression analysis using the empirical Bayes Limma approach. When evaluated on two spike-in data sets the RT-segmented approaches outperformed conventional approaches by detecting more peptides (8-36%) without loss of precision. Furthermore, differential expression analysis using the Limma approach consistently increased recall (2-35%) compared to analysis of variance. The combination of RT-normalization and Limma was in one case able to distinguish 108% (2597 vs 1249) more spike-in peptides compared to traditional approaches. NormalyzerDE provides widely usable tools for performing normalization and evaluating the outcome and makes calculation of subsequent differential expression statistics straightforward. The program is available as a web server at http://quantitativeproteomics.org/normalyzerde .

Identification of genes regulating traits targeted for domestication of field cress (Lepidium campestre) as a biennial and perennial oilseed crop.

BACKGROUND: The changing climate and the desire to use renewable oil sources necessitate the development of new oilseed crops. Field cress (Lepidium campestre) is a species in the Brassicaceae family that has been targeted for domestication not only as an oilseed crop that produces seeds with a desirable industrial oil quality but also as a cover/catch crop that provides valuable ecosystem services. Lepidium is closely related to Arabidopsis and display significant proportions of syntenic regions in their genomes. Arabidopsis genes are among the most characterized genes in the plant kingdom and, hence, comparative genomics of Lepidium-Arabidopsis would facilitate the identification of Lepidium candidate genes regulating various desirable traits. RESULTS: Homologues of 30 genes known to regulate vernalization, flowering time, pod shattering, oil content and quality in Arabidopsis were identified and partially characterized in Lepidium. Alignments of sequences representing field cress and two of its closely related perennial relatives: L. heterophyllum and L. hirtum revealed 243 polymorphic sites across the partial sequences of the 30 genes, of which 95 were within the predicted coding regions and 40 led to a change in amino acids of the target proteins. Within field cress, 34 polymorphic sites including nine non-synonymous substitutions were identified. The phylogenetic analysis of the data revealed that field cress is more closely related to L. heterophyllum than to L. hirtum. CONCLUSIONS: There is significant variation within and among Lepidium species within partial sequences of the 30 genes known to regulate traits targeted in the present study. The variation within these genes are potentially useful to speed-up the process of domesticating field cress as future oil crop. The phylogenetic relationship between the Lepidium species revealed in this study does not only shed some light on Lepidium genome evolution but also provides important information to develop efficient schemes for interspecific hybridization between different Lepidium species as part of the domestication efforts.

Patient-Derived Xenograft Models Reveal Intratumor Heterogeneity and Temporal Stability in Neuroblastoma.

Patient-derived xenografts (PDX) and the Avatar, a single PDX mirroring an individual patient, are emerging tools in preclinical cancer research. However, the consequences of intratumor heterogeneity for PDX modeling of biomarkers, target identification, and treatment decisions remain underexplored. In this study, we undertook serial passaging and comprehensive molecular analysis of neuroblastoma orthotopic PDXs, which revealed strong intrinsic genetic, transcriptional, and phenotypic stability for more than 2 years. The PDXs showed preserved neuroblastoma-associated gene signatures that correlated with poor clinical outcome in a large cohort of patients with neuroblastoma. Furthermore, we captured spatial intratumor heterogeneity using ten PDXs from a single high-risk patient tumor. We observed diverse growth rates, transcriptional, proteomic, and phosphoproteomic profiles. PDX-derived transcriptional profiles were associated with diverse clinical characteristics in patients with high-risk neuroblastoma. These data suggest that high-risk neuroblastoma contains elements of both temporal stability and spatial intratumor heterogeneity, the latter of which complicates clinical translation of personalized PDX-Avatar studies into preclinical cancer research.Significance: These findings underpin the complexity of PDX modeling as a means to advance translational applications against neuroblastoma. Cancer Res; 78(20); 5958-69. ©2018 AACR.