Omics Tools Research Articles

NPOmix: A machine learning classifier to connect mass spectrometry fragmentation data to biosynthetic gene clusters.

Microbial specialized metabolites are an important source of and inspiration for many pharmaceuticals, biotechnological products and play key roles in ecological processes. Untargeted metabolomics using liquid chromatography coupled with tandem mass spectrometry is an efficient technique to access metabolites from fractions and even environmental crude extracts. Nevertheless, metabolomics is limited in predicting structures or bioactivities for cryptic metabolites. Efficiently linking the biosynthetic potential inferred from (meta)genomics to the specialized metabolome would accelerate drug discovery programs by allowing metabolomics to make use of genetic predictions. Here, we present a k-nearest neighbor classifier to systematically connect mass spectrometry fragmentation spectra to their corresponding biosynthetic gene clusters (independent of their chemical class). Our new pattern-based genome mining pipeline links biosynthetic genes to metabolites that they encode for, as detected via mass spectrometry from bacterial cultures or environmental microbiomes. Using paired datasets that include validated genes-mass spectral links from the Paired Omics Data Platform, we demonstrate this approach by automatically linking 18 previously known mass spectra (17 for which the biosynthesis gene clusters can be found at the MIBiG database plus palmyramide A) to their corresponding previously experimentally validated biosynthetic genes (e.g., via nuclear magnetic resonance or genetic engineering). We illustrated a computational example of how to use our Natural Products Mixed Omics (NPOmix) tool for siderophore mining that can be reproduced by the users. We conclude that NPOmix minimizes the need for culturing (it worked well on microbiomes) and facilitates specialized metabolite prioritization based on integrative omics mining.

Multi-omics studies reveal ameliorating effects of physical exercise on neurodegenerative diseases.

Neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and Huntington's disease, are heavy burdens to global health and economic development worldwide. Mounting evidence suggests that exercise, a type of non-invasive intervention, has a positive impact on the life quality of elderly with neurodegenerative diseases. X-omics are powerful tools for mapping global biochemical changes in disease and treatment. Three major databases were searched related to current studies in exercise intervention on neurodegenerative diseases using omics tools, including metabolomics, metagenomics, genomics, transcriptomics, and proteomics. We summarized the omics features and potential mechanisms associated with exercise and neurodegenerative diseases in the current studies. Three main mechanisms by which exercise affects neurodegenerative diseases were summed up, including adult neurogenesis, brain-derived neurotrophic factor (BDNF) signaling, and short-chain fatty acids (SCFAs) metabolism. Overall, there is compelling evidence that exercise intervention is a feasible way of preventing the onset and alleviating the severity of neurodegenerative diseases. These studies highlight the importance of exercise as a complementary approach to the treatment and intervention of neurodegenerative diseases in addition to traditional treatments. More mechanisms on exercise interventions for neurodegenerative diseases, the specification of exercise prescriptions, and differentiated exercise programs should be explored so that they can actually be applied to the clinic.

Multi-Omics Analysis Reveals Clinical Value and Possible Mechanisms of ATAD1 Down-Regulation in Human Prostate Adenocarcinoma.

Prostate adenocarcinoma (PRAD) is the most common histological subtype of prostate cancer. Post-treatment biochemical recurrence is a challenging issue. ATAD1 (ATPase Family AAA Domain Containing 1) plays a vital role in mitochondrial proteostasis and apoptosis activity, while its clinical value in PRAD and its impact on the tumor microenvironment (TME) remain unanswered. In this study, we aimed to investigate the clinical value and possible mechanisms of ATAD1 in PRAD via multi-omics analysis. Using cBioPortal, we confirmed that ATAD1 alteration was associated with gene expression and unfavorable DFS. Deep deletion predominantly occurred in PRAD. By integrating DriverDBv3 and GEPIA2, we noted ATAD1 downregulation in PRAD tissues compared to normal tissues, associated with unfavorable DFS in PRAD patients. DNA repair genes ATM, PARP1and BRCA2 had positive associations with ATAD1 expression. We found that the generalization value of ATAD1 could be applied to other cancers such as KIRC and UCEC. In addition, LinkedOmics identified that the functional involvement of ATAD1 participates in mitochondrial structure and cell cycle progression. Using TIMER analysis, we demonstrated that ATAD1 downregulation correlated with an immunosuppressive TME. Furthermore, we accessed a GSE55062 dataset on UALCAN and discovered the involvement of ERG-mediated transcriptional repression on ATAD1 downregulation. Cross-association screening of shATAD1 efficacy vs. altered mRNAs identified 190 perturbed mRNAs. Then, functional enrichment analysis using the Metascape omics tool recognized that shATAD1-perturbed mRNAs are primarily in charge of the activation of Wnt/β-catenin pathway and lipid metabolic processes. In conclusion, multi-omics results reveal that ATAD1 downregulation is a clinical biomarker for pathological diagnosis and prognosis for patients with PRAD. Reduced ATAD1 may be involved in the enhanced activity of mitochondria and cell cycle, as well as possibly shaping an immunosuppressive TME. ERG serves as an upstream transcriptional repressor of ATAD1. Downstream mechanisms of ATAD1 are involved in Wnt/β-catenin pathway and lipid metabolic processes.

An omics evolutionary perspective on phytophagous insect–host plant interactions in Anastrepha obliqua: a review

AbstractPhytophagous insects have a close relationship with their host plants. For this reason, their interactions can lead to important changes in insect population dynamics and evolutionary trajectories. Next generation sequencing (NGS) has provided an opportunity to analyze omics data on a large scale, facilitating the change from a classical genetics approach to a more holistic understanding of the underlying molecular mechanisms of host plant use by insects. Most studies have been carried out on model species in Holarctic and temperate zones. In tropical zones, however, the effects of use of various host plants on evolutionary insect history is less understood. In the current review, we describe how omics methodologies help us to understand phytophagous insect–host plant interactions from an evolutionary perspective, using as example the Neotropical phytophagous insect West Indian fruit fly, Anastrepha obliqua (Macquart) (Diptera: Tephritidae), an economically important fruit crop pest in the Americas. Anastrepha obliqua could adopt a generalist or a specialist lifestyle. We first review the adaptive molecular mechanisms of phytophagous insects to host plants, and then describe the main tools to study phytophagous insect–host plant interactions in the era of omics sciences. The omics approaches will advance the understanding of insect molecular mechanisms and their influence on diversification and evolution. Finally, we discuss the importance of a multidisciplinary approach that integrates the use of omics tools and other, more classical methodologies in evolutionary studies.

Spatially resolved epigenomic profiling of single cells in complex tissues

The recent development of spatial omics methods has enabled single-cell profiling of the transcriptome and 3D genome organization with high spatial resolution. Expanding the repertoire of spatial omics tools, a spatially resolved single-cell epigenomics method will accelerate understanding of the spatial regulation of cell and tissue functions. Here, we report a method for spatially resolved epigenomic profiling of single cells using in situ tagmentation and transcription followed by multiplexed imaging. We demonstrated the ability to profile histone modifications marking active promoters, putative enhancers, and silent promoters in individual cells, and generated high-resolution spatial atlas of hundreds of active promoters and putative enhancers in embryonic and adult mouse brains. Our results suggested putative promoter-enhancer pairs and enhancer hubs regulating developmentally important genes. We envision this approach will be generally applicable to spatial profiling of epigenetic modifications and DNA-binding proteins, advancing our understanding of how gene expression is spatiotemporally regulated by the epigenome.

Integrative Serum Metabolic Fingerprints Based Multi-Modal Platforms for Lung Adenocarcinoma Early Detection and Pulmonary Nodule Classification.

Identification of novel non-invasive biomarkers is critical for the early diagnosis of lung adenocarcinoma (LUAD), especially for the accurate classification of pulmonary nodule. Here, a multiplexed assay is developed on an optimized nanoparticle-based laser desorption/ionization mass spectrometry platform for the sensitive and selective detection of serum metabolic fingerprints (SMFs). Integrative SMFs based multi-modal platforms are constructed for the early detection of LUAD and the classification of pulmonary nodule. The dual modal model, metabolic fingerprints with protein tumor marker neural network (MP-NN), integrating SMFs with protein tumor marker carcinoembryonic antigen (CEA) via deep learning, shows superior performance compared with the single modal model Met-NN (p < 0.001). Based on MP-NN, the tri modal model MPI-RF integrating SMFs, tumor marker CEA, and image features via random forest demonstrates significantly higher performance than the clinical models (Mayo Clinic and Veterans Affairs) and the image artificial intelligence in pulmonary nodule classification (p < 0.001). The developed platforms would be promising tools for LUAD screening and pulmonary nodule management, paving the conceptual and practical foundation for the clinical application of omics tools.

Recent metabolomic developments for antimalarial drug discovery.

Malaria is a parasitic disease that remains a global health issue, responsible for a significant death and morbidity toll. Various factors have impacted the use and delayed the development of antimalarial therapies, such as the associated financial cost and parasitic resistance. In order to discover new drugs and validate parasitic targets, a powerful omics tool, metabolomics, emerged as a reliable approach. However, as a fairly recent method in malaria, new findings are timely and original practices emerge frequently. This review aims to discuss recent research towards the development of new metabolomic methods in the context of uncovering antiplasmodial mechanisms of action in vitro and to point out innovative metabolic pathways that can revitalize the antimalarial pipeline.

Holistic Integration of Omics Tools for Precision Nutrition in Health and Disease.

The combination of multiple omics approaches has emerged as an innovative holistic scope to provide a more comprehensive view of the molecular and physiological events underlying human diseases (including obesity, dyslipidemias, fatty liver, insulin resistance, and inflammation), as well as for elucidating unique and specific metabolic phenotypes. These omics technologies include genomics (polymorphisms and other structural genetic variants), epigenomics (DNA methylation, histone modifications, long non-coding RNA, telomere length), metagenomics (gut microbiota composition, enterotypes), transcriptomics (RNA expression patterns), proteomics (protein quantities), and metabolomics (metabolite profiles), as well as interactions with dietary/nutritional factors. Although more evidence is still necessary, it is expected that the incorporation of integrative omics could be useful not only for risk prediction and early diagnosis but also for guiding tailored dietary treatments and prognosis schemes. Some challenges include ethical and regulatory issues, the lack of robust and reproducible results due to methodological aspects, the high cost of omics methodologies, and high-dimensional data analyses and interpretation. In this review, we provide examples of system biology studies using multi-omics methodologies to unravel novel insights into the mechanisms and pathways connecting the genotype to clinically relevant traits and therapy outcomes for precision nutrition applications in health and disease.

Analysis of the Human Protein Atlas Weakly Supervised Single-Cell Classification competition

While spatial proteomics by fluorescence imaging has quickly become an essential discovery tool for researchers, fast and scalable methods to classify and embed single-cell protein distributions in such images are lacking. Here, we present the design and analysis of the results from the competition Human Protein Atlas – Single-Cell Classification hosted on the Kaggle platform. This represents a crowd-sourced competition to develop machine learning models trained on limited annotations to label single-cell protein patterns in fluorescent images. The particular challenges of this competition include class imbalance, weak labels and multi-label classification, prompting competitors to apply a wide range of approaches in their solutions. The winning models serve as the first subcellular omics tools that can annotate single-cell locations, extract single-cell features and capture cellular dynamics.

Lipidomics of cyclophosphamide 4-hydroxylation in patients receiving post-transplant cyclophosphamide.

Biomarker-guided dosing may improve the efficacy and toxicity of cyclophosphamide (CY); however, clinical studies evaluating their association with the area under the plasma concentration-time curve (AUC) of CY and its metabolites are time- and resource-intensive. Therefore, we sought to identify lipidomic biomarkers associated with the time-varying differences in CY formation clearance to 4-hydroxycyclophosphamide (4HCY), the principal precursor to CY's cytotoxic metabolite. Hematopoietic cell transplant (HCT) patients receiving post-transplant CY (PT-CY) were enrolled, cohort 1 (n=25) and cohort 2 (n=26) donating longitudinal blood samples before they started HCT (pre-HCT), before infusion of the donor allograft (pre-graft), before the first dose of PT-CY (pre-CY) and 24 h after the first dose of PT-CY (24-h post-CY) which is also immediately before the second dose of CY. A total of 409 and 387 lipids were quantitated in the two cohorts, respectively. Associations between lipids, individually and at a class level, and the ratio of 4HCY/CY AUC (i.e., 4HCY formation clearance) were evaluated using linear regression with a false discovery rate <0.05. There were no individual lipids that passed control for false discovery at any time point. These results demonstrate the feasibility of lipidomics, but future studies in larger samples with multiple omic tools are warranted to optimize CY dosing in HCT.

Pharmacometabolomics-Based Translational Biomarkers: How to Navigate the Data Ocean.

Metabolome is the end point of the genome-environment interplay, and enables an important holistic overview of individual adaptability and host responses to environmental, ecological, as well as endogenous changes such as disease. Pharmacometabolomics is the application of metabolome knowledge to decipher the mechanisms of interindividual and intraindividual variations in drug efficacy and safety. Pharmacometabolomics also contributes to prediction of drug treatment outcomes on the basis of baseline (predose) and postdose metabotypes through mathematical modeling. Thus, pharmacometabolomics is a strong asset for a diverse community of stakeholders interested in theory and practice of evidence-based and precision/personalized medicine: academic researchers, public health scholars, health professionals, pharmaceutical, diagnostics, and biotechnology industries, among others. In this expert review, we discuss pharmacometabolomics in four contexts: (1) an interdisciplinary omics tool and field to map the mechanisms and scale of interindividual variability in drug effects, (2) discovery and development of translational biomarkers, (3) advance digital biomarkers, and (4) empower drug repurposing, a field that is increasingly proving useful in the current era of Covid-19. As the applications of pharmacometabolomics are growing rapidly in the current postgenome era, next-generation proteomics and metabolomics follow the example of next-generation sequencing analyses. Pharmacometabolomics can also empower data reliability and reproducibility through multiomics integration strategies, which use each data layer to correct, connect with, and inform each other. Finally, we underscore here that contextual data remain crucial for precision medicine and drug development that stand the test of time and clinical relevance.

Management of Rhizosphere Microbiota and Plant Production under Drought Stress: A Comprehensive Review.

Drought generates a complex scenario worldwide in which agriculture should urgently be reframed from an integrative point of view. It includes the search for new water resources and the use of tolerant crops and genotypes, improved irrigation systems, and other less explored alternatives that are very important, such as biotechnological tools that may increase the water use efficiency. Currently, a large body of evidence highlights the role of specific strains in the main microbial rhizosphere groups (arbuscular mycorrhizal fungi, yeasts, and bacteria) on increasing the drought tolerance of their host plants through diverse plant growth-promoting (PGP) characteristics. With this background, it is possible to suggest that the joint use of distinct PGP microbes could produce positive interactions or additive beneficial effects on their host plants if their co-inoculation does not generate antagonistic responses. To date, such effects have only been partially analyzed by using single omics tools, such as genomics, metabolomics, or proteomics. However, there is a gap of information in the use of multi-omics approaches to detect interactions between PGP and host plants. This approach must be the next scale-jump in the study of the interaction of soil–plant–microorganism. In this review, we analyzed the constraints posed by drought in the framework of an increasing global demand for plant production, integrating the important role played by the rhizosphere biota as a PGP agent. Using multi-omics approaches to understand in depth the processes that occur in plants in the presence of microorganisms can allow us to modulate their combined use and drive it to increase crop yields, improving production processes to attend the growing global demand for food.

Denitrification‐Driven Transcription and Enzyme Production at the River‐Groundwater Interface: Insights From Reactive‐Transport Modeling

AbstractMolecular‐biological data and omics tools have increasingly been used to characterize microorganisms responsible for the turnover of reactive compounds in the environment, such as reactive‐nitrogen species in groundwater. While transcripts of functional genes and enzymes are used as measures of microbial activity, it is not yet clear how they are quantitatively related to actual turnover rates under variable environmental conditions. As an example application, we consider the interface between rivers and groundwater which has been identified as a key driver for the turnover of reactive‐nitrogen compounds, that cause eutrophication of rivers and endanger drinking water production from groundwater. In the absence of measured data, we developed a reactive‐transport model for denitrification that simultaneously predicts the distributions of functional‐gene transcripts, enzymes, and reaction rates. Applying the model, we evaluate the response of transcripts and enzymes at the river‐groundwater interface to stable and dynamic hydrogeochemical regimes. While functional‐gene transcripts respond to short‐term (diurnal) fluctuations of substrate availability and oxygen concentrations, enzyme concentrations are stable over such time scales. The presence of functional‐gene transcripts and enzymes globally coincides with the zones of active denitrification. However, transcript and enzyme concentrations do not directly translate into denitrification rates in a quantitative way because of nonlinear effects and hysteresis caused by variable substrate availability and oxygen inhibition. Based on our simulations, we suggest that molecular‐biological data should be combined with aqueous geochemical data, which can typically be obtained at higher spatial and temporal resolution, to parameterize and calibrate reactive‐transport models.

Strategic management of contaminated water bodies: Omics, genome-editing and other recent advances in phytoremediation

Due to excessive industrialization, urbanization and rising level of pollutants, the water bodies are continuously exposed to diverse group of toxic pollutants whose concentration, chemical forms and bioavailability in the aqueous system are governed by natural geochemical cycles as well as human activities. Chemical form or species of these toxic compounds or heavy metals is governed by the prevailing specific physical and chemical parameters like pH, salinity, dissolved organics, hardness and sedimentary load which impacts their mobility, bio-availability and hence toxicity. To overcome the challenging situation, an increased emphasis has been given on in-situ innovative technologies like phytoremediation, capable of detoxification of contaminated water bodies. The use of inherent aquatic plants along with recent omics tools can improve their phytoremediation ability to a great extent. These plants have an extensive root system which can filter and immobilize sediments, contaminants, fertilizer and pesticide run-off thereby reducing water pollution. Thus, the assortment and improvement of plants phytoremediation ability using the modern biotechnological tools and techniques, can be a suitable approach for successful remediation of contaminated water bodies. This review article focuses on the different biotechnological strategems like omics, proteomics, genomics, metabolomics and CRISPR in order to ameliorate the phytoremediation potential of the aquatic plants. The recent ground-breaking advancements to achieve desired genetic modification uses, CRISPR technology, to transfer a target set of genes to be assimilated into the plant genome of interest. Two variants of this technology, i.e. CRISPR-Cas9 as well as CRISPR-Cpf1 can be used for producing knock-outs, making gene substitutions, and targeting transcription and its regulation in the particular plant genome, to improve its phytoremediation efficiency.

Scientific Advances in Biostimulation Reported in the 5th Biostimulant World Congress

Biostimulants are agronomic products that have become highly important in agriculture, as they are formulated with substances capable of stimulating physiological and biochemical processes in plants that help them adapt to different detrimental environmental conditions such as drought, salinity, high temperatures, nutritional deficiencies, etc. in such a way that the crops, under these conditions, maintain a good agronomic yield and quality of harvest. Every two years, the International Congress on Biostimulants is held with the aim of publicizing the most innovative and recent advances in every relevant type of product: new active ingredients, modes of action, cultivation protocols, test platforms and phenotyping, use of analytical omics tools, etc. In December 2021, the 5th World Congress on Biostimulants was held in Miami (Florida, USA). This publication summarizes the most relevant results that were presented at this congress, in which biostimulants formulated with algae extracts and amino acids stood out in a number of presentations. The various studies presented were carried out on diverse crops such as apple, blackberry, carrot, celery, cherry, corn, grape (table and wine), olive, pear, pepper, potato, rapeseed, spinach, sunflower, soybean, tomato, and wheat. The future lines of the new generation of biostimulants were also marked.

Application of Omics Tools in Designing and Monitoring Marine Protected Areas For a Sustainable Blue Economy.

A key component of the global blue economy strategy is the sustainable extraction of marine resources and conservation of marine environments through networks of marine protected areas (MPAs). Connectivity and representativity are essential factors that underlie successful implementation of MPA networks, which can safeguard biological diversity and ecosystem function, and ultimately support the blue economy strategy by balancing ocean use with conservation. New “big data” omics approaches, including genomics and transcriptomics, are becoming essential tools for the development and maintenance of MPA networks. Current molecular omics techniques, including population-scale genome sequencing, have direct applications for assessing population connectivity and for evaluating how genetic variation is represented within and among MPAs. Effective baseline characterization and long-term, scalable, and comprehensive monitoring are essential for successful MPA management, and omics approaches hold great promise to characterize the full range of marine life, spanning the microbiome to megafauna across a range of environmental conditions (shallow sea to the deep ocean). Omics tools, such as eDNA metabarcoding can provide a cost-effective basis for biodiversity monitoring in large and remote conservation areas. Here we provide an overview of current omics applications for conservation planning and monitoring, with a focus on metabarcoding, metagenomics, and population genomics. Emerging approaches, including whole-genome sequencing, characterization of genomic architecture, epigenomics, and genomic vulnerability to climate change are also reviewed. We demonstrate that the operationalization of omics tools can enhance the design, monitoring, and management of MPAs and thus will play an important role in a modern and comprehensive blue economy strategy.

Strategy for Microscale Extraction and Proteome Profiling of Peripheral Blood Mononuclear Cells.

Peripheral blood mononuclear cells (PBMCs) play vital roles in physiological and pathological processes and represent a rich source for disease monitoring. Typical molecular profiling on PBMCs involves the sorting of cell subsets and thus requires a large volume of peripheral blood (PB), which impedes the clinical practicability of omics tools in PBMC measurements. It would be clinically invaluable to develop a convenient approach for preparing PBMCs from small volumes of PB and for deep proteome profiling of PBMCs. To this end, here, we designed an apparatus (PBMC-mCap) for microscale enrichment and proteome analysis of PBMCs, which pushed the needed PB volume from the normal 2 mL or higher to 100 μL or lower, comparable to the volume of a drop of finger blood. A PBMC-specific mass spectra library containing 8869 proteins and 121,956 peptides was further built, which, in combination with the optimized data-independent acquisition strategy, helped to identify 6000 and 6500 proteins from PBMCs with 100 μL and 1 mL of PB as initial materials, respectively. Further application of the strategy for PBMC proteomes revealed a steady difference between gender (male vs female) and upon stimulus (COVID-19 vaccination). For the latter, we observed differentially expressed genes and pathways involving the activation of immune cells, including the NF-κB pathway, inflammation response, and antiviral response. Our strategy for the proteome analysis of microscale PBMCs may provide a convenient clinical toolkit for disease diagnosis and healthy state monitoring.

Molecular Markers of Kidney Transplantation Outcome: Current Omics Tools and Future Developments.

Purpose of review: The emerging field of molecular predictive medicine is aiming to change the traditional medical approach in renal transplantation. Many studies have explored potential biomarker molecules with predictive properties in renal transplantation, issued from omics research. Herein, we review the biomarker molecules of four technologies (i.e., Genomics, Transcriptomics, Proteomics, and Metabolomics) associated with favorable kidney transplant outcomes. Recent findings: Several panels of molecules have been associated with the outcome that the majority of markers are related to inflammation and immune response; although. other molecular ontologies are also represented, such as proteasome, growth, regeneration, and drug metabolism. Throughout this review, we highlight the lack of properly validated statistical demonstration. Indeed, the most preeminent molecular panels either remain at the limited size study stage or are not confirmed during large-scale studies. At the core of this problem, we identify the methodological shortcomings and propose a comprehensive workflow for discovery and validation of molecular biomarkers that aims to improve the relevance of these tools in the future. Summary: Overall, adopting a patient management through omics approach could bring remarkable improvement to transplantation success. An increased effort and investment between scientists, medical biologists, and clinicians seem to be the path toward a proper solution.

Can omic tools help generate alternative newer sources of edible seed oil?

There are three pathways for triacylglycerol (TAG) biosynthesis: De novo TAG biosynthesis, phosphatidylcholine‐derived biosynthesis, and cytosolic TAG biosynthesis. Variability in fatty acid composition is mainly associated with phosphatidylcholine‐derived TAG pathway. Mobilization of TAG‐formed through cytosolic pathway into lipid droplets is yet unknown. There are multiple regulatory checkpoints starting from acetyl‐CoA carboxylase to the lipid droplet biogenesis in TAG biosynthesis. Although a primary metabolism, only a few species synthesize oil in seeds for storage, and less than 10 species are commercially exploited. To meet out the growing demand for oil, diversifying into newer sources is the only choice left. The present review highlights the potential strategies targeting species like Azadirachta, Callophyllum, Madhuca, Moringa, Pongamia, Ricinus, and Simarouba, which are not being used for eating but are otherwise high yielding (ranging from 1.5 to 20 tons per hectare) with seeds having a high oil content (40–60%). Additionally, understanding the toxin biosynthesis in Ricinus and Simarouba would be useful in developing toxin‐free oil plants. Realization of the importance of cell cultures as “oil factories” is not too far into the future and would soon be a commercially viable option for producing oils in vitro, round the clock.

Development of a ribosome profiling protocol to study translation in Kluyveromyces marxianus.

Kluyveromyces marxianus is an interesting and important yeast because of particular traits such as thermotolerance and rapid growth, and for applications in food and industrial biotechnology. For both understanding its biology and developing bioprocesses, it is important to understand how K. marxianus responds and adapts to changing environments. For this, a full suite of omics tools to measure and compare global patterns of gene expression and protein synthesis is needed. We report here the development of a ribosome profiling method for K. marxianus, which allows codon resolution of translation on a genome-wide scale by deep sequencing of ribosome locations on mRNAs. To aid in the analysis and sharing of ribosome profiling data, we added the K. marxianus genome as well as transcriptome and ribosome profiling data to the publicly accessible GWIPS-viz and Trips-Viz browsers. Users are able to upload custom ribosome profiling and RNA-Seq data to both browsers, therefore allowing easy analysis and sharing of data. We also provide a set of step-by-step protocols for the experimental and bioinformatic methods that we developed.