Machine learning methods and harmonized datasets improve immunogenic neoantigen prediction.

The accurate selection of neoantigens that bind to class I human leukocyte antigen (HLA) and are recognized by autologous T cells is a crucial step in many cancer immunotherapy pipelines. We reprocessed whole-exome sequencing and RNA sequencing (RNA-seq) data from 120 cancer patients from two external large-scale neoantigen immunogenicity screening assays combined with an in-house dataset of 11 patients and identified 46,017 somatic single-nucleotide variant mutations and 1,781,445 neo-peptides, of which 212 mutations and 178 neo-peptides were immunogenic. Beyond features commonly used for neoantigen prioritization, factors such as the location of neo-peptides within protein HLA presentation hotspots, binding promiscuity, and the role of the mutated gene in oncogenicity were predictive for immunogenicity. The classifiers accurately predicted neoantigen immunogenicity across datasets and improved their ranking by up to 30%. Besides insights into machine learning methods for neoantigen ranking, we have provided homogenized datasets valuable for developing and benchmarking companion algorithms for neoantigen-based immunotherapies.

Mapping Degradation Signals and Pathways in a Eukaryotic N-terminome.

Most eukaryotic proteins are N-terminally acetylated. This modification can be recognized as a signal for selective protein degradation (degron) by the N-end rule pathways. However, the prevalence and specificity of such degrons in the proteome are unclear. Here, by systematically examining how protein turnover is affected by N-terminal sequences, we perform a comprehensive survey of degrons in the yeast N-terminome. We find that approximately 26% of nascent protein N termini encode cryptic degrons. These degrons exhibit high hydrophobicity and are frequently recognized by the E3 ubiquitin ligase Doa10, suggesting a role in protein quality control. In contrast, N-terminal acetylation rarely functions as a degron. Surprisingly, we identify two pathways where N-terminal acetylation has the opposite function and blocks protein degradation through the E3 ubiquitin ligase Ubr1. Our analysis highlights the complexity of N-terminal degrons and argues that hydrophobicity, not N-terminal acetylation, is the predominant feature of N-terminal degrons in nascent proteins.

Mammalian RAD52 Functions in Break-Induced Replication Repair of Collapsed DNA Replication Forks.

Human cancers are characterized by the presence of oncogene-induced DNA replication stress (DRS), making them dependent on repair pathways such as break-induced replication (BIR) for damaged DNA replication forks. To better understand BIR, we performed a targeted siRNA screen for genes whose depletion inhibited G1 to S phase progression when oncogenic cyclin E was overexpressed. RAD52, a gene dispensable for normal development in mice, was among the top hits. In cells in which fork collapse was induced by oncogenes or chemicals, the Rad52 protein localized to DRS foci. Depletion of Rad52 by siRNA or knockout of the gene by CRISPR/Cas9 compromised restart of collapsed forks and led to DNA damage in cells experiencing DRS. Furthermore, in cancer-prone, heterozygous APC mutant mice, homozygous deletion of the Rad52 gene suppressed tumor growth and prolonged lifespan. We therefore propose that mammalian RAD52 facilitates repair of collapsed DNA replication forks in cancer cells.

Decreased rotator cuff muscle cross-sectional areas in subjects with adhesive capsulitis: a study comparing male and female subjects.

OBJECTIVE: To compare rotator cuff (RC) muscle cross-sectional areas (CSA) in subjects with adhesive capsulitis (AC) to age- and sex-matched controls. MATERIALS AND METHODS: We retrospectively analyzed 97 shoulder MRIs or MR arthrography studies, of which 42 were clinically diagnosed with AC (27 female, 15 male) and 55 were age- and sex-matched controls (38 female, 17 male). All AC subjects underwent imaging ≥ 6 months after symptom onset. All imaging was examined to exclude RC full-thickness tears and prior surgery. A standardized T1 sagittal MR image was segmented in each subject to obtain the CSA of subscapularis (SSC), supraspinatus (SSP), and infraspinatus (ISP) muscles. Differences in CSAs between AC and control subjects were analyzed by sex (females and males separately) and all subjects combined. RESULTS: AC females had significantly decreased SSC (P = 0.002) and total (P = 0.006) CSAs compared to controls. Male AC subjects showed decreased SSC (P = 0.044), SSP (P = 0.001), and total (P = 0.005) CSAs. Across all subjects, male and female, the AC cohort had significantly decreased SSC (P = 0.019) and total (P = 0.029) CSAs compared to controls. CONCLUSION: Decreased RC muscle CSAs were present in AC subjects with ≥ 6 months of symptom duration, with decreased SSC and total CSAs in male and female subjects, and decreased SSP CSA in males.

Cross-sectional areas of rotator cuff muscles in males without tears on shoulder MRI.

OBJECTIVE: To establish reference values of rotator cuff (RC) cross sectional area (CSA) in males. MATERIALS AND METHODS: We retrospectively analyzed shoulder MRIs from 500 patients aged 13-78 years, grouped as follows (N=100 in each): <20, 20-30, 30-40, 40-50, >50 years. All examinations were reviewed to exclude prior surgery, tears, or significant RC pathology. We segmented a standardized T1 sagittal MR image in each case to obtain CSA of supraspinatus (SUP), infraspinatus/teres minor (INF), and subscapularis (SUB) muscles. Across age groups, we recorded individual and total muscle CSA. We also performed ratios between individual muscle CSA and total CSA to examine total muscle mass contribution over age groups. We tested for differences between age groups controlled for BMI. RESULTS: CSAs for SUP, INF, SUB, and total RC CSA were lower in subjects >50 years compared to all other groups (P<0.003 for all comparisons), persisting after controlling for BMI (P<0.03). Relative contribution of SUP CSA to total RC CSA was stable across age groups (P>0.32). INF CSA relative to total RC CSA increased with age, whereas SUB decreased (P<0.005). Subjects >50 years showed lower SUP (-15%), INF (-6%), and SUB (-21%) CSA, when compared to mean CSAs of all subjects <50 years. Total RC CSA significantly correlated with age (r=-0.34, P<0.001), persisting after controlling for BMI (r=-0.42, P<0.001). CONCLUSION: RC muscles in male subjects with no tears on MRI show decreasing CSA with age, independent of BMI.

Leveraging Remote Sensing Data for Yield Prediction with Deep Transfer Learning.

Remote sensing data represent one of the most important sources for automized yield prediction. High temporal and spatial resolution, historical record availability, reliability, and low cost are key factors in predicting yields around the world. Yield prediction as a machine learning task is challenging, as reliable ground truth data are difficult to obtain, especially since new data points can only be acquired once a year during harvest. Factors that influence annual yields are plentiful, and data acquisition can be expensive, as crop-related data often need to be captured by experts or specialized sensors. A solution to both problems can be provided by deep transfer learning based on remote sensing data. Satellite images are free of charge, and transfer learning allows recognition of yield-related patterns within countries where data are plentiful and transfers the knowledge to other domains, thus limiting the number of ground truth observations needed. Within this study, we examine the use of transfer learning for yield prediction, where the data preprocessing towards histograms is unique. We present a deep transfer learning framework for yield prediction and demonstrate its successful application to transfer knowledge gained from US soybean yield prediction to soybean yield prediction within Argentina. We perform a temporal alignment of the two domains and improve transfer learning by applying several transfer learning techniques, such as L2-SP, BSS, and layer freezing, to overcome catastrophic forgetting and negative transfer problems. Lastly, we exploit spatio-temporal patterns within the data by applying a Gaussian process. We are able to improve the performance of soybean yield prediction in Argentina by a total of 19% in terms of RMSE and 39% in terms of R2 compared to predictions without transfer learning and Gaussian processes. This proof of concept for advanced transfer learning techniques for yield prediction and remote sensing data in the form of histograms can enable successful yield prediction, especially in emerging and developing countries, where reliable data are usually limited.

Deep Interpolation of Remote Sensing Land Surface Temperature Data with Partial Convolutions.

Land Surface Temperature (LST) is an important resource for a variety of tasks. The data are mostly free of charge and combine high spatial and temporal resolution with reliable data collection over a historical timeframe. When remote sensing is used to provide LST data, such as the MODA11 product using information from the MODIS sensors attached to NASA satellites, data acquisition can be hindered by clouds or cloud shadows, occluding the sensors' view on different areas of the world. This makes it difficult to take full advantage of the high resolution of the data. A common solution to interpolating LST data is statistical interpolation methods, such as fitting polynomials or thin plate spine interpolation. These methods have difficulties in incorporating additional knowledge about the research area and learning local dependencies that can help with the interpolation process. We propose a novel approach to interpolating remote sensing LST data in a fixed research area considering local ground-site air temperature measurements. The two-step approach consists of learning the LST from air temperature measurements, where the ground-site weather stations are located, and interpolating the remaining missing values with partial convolutions within a U-Net deep learning architecture. Our approach improves the interpolation of LST for our research area by 44% in terms of RMSE, when compared to state-of-the-art statistical methods. Due to the use of air temperature, we can provide coverage of 100%, even when no valid LST measurements were available. The resulting gapless coverage of high resolution LST data will help unlock the full potential of remote sensing LST data.

Unified and Standardized Mass Spectrometry Data Processing in Python Using spectrum_utils.

spectrum_utils is a Python package for mass spectrometry data processing and visualization. Since its introduction, spectrum_utils has grown into a fundamental software solution that powers various applications in proteomics and metabolomics, ranging from spectrum preprocessing prior to spectrum identification and machine learning applications to spectrum plotting from online data repositories and assisting data analysis tasks for dozens of other projects. Here, we present updates to spectrum_utils, which include new functionality to integrate mass spectrometry community data standards, enhanced mass spectral data processing, and unified mass spectral data visualization in Python. spectrum_utils is freely available as open source at https://github.com/bittremieux/spectrum_utils.

Two-year Skeletal Effects of Sleeve Gastrectomy in Adolescents with Obesity Assessed with Quantitative CT and MR Spectroscopy.

Background Sleeve gastrectomy (SG) is effective in the treatment of cardiometabolic complications of obesity but is associated with bone loss. Purpose To determine the long-term effects of SG on vertebral bone strength, density, and bone marrow adipose tissue (BMAT) in adolescents and young adults with obesity. Materials and Methods This 2-year prospective nonrandomized longitudinal study enrolled adolescents and young adults with obesity who underwent either SG (SG group) or dietary and exercise counseling without surgery (control group) at an academic medical center from 2015 to 2020. Participants underwent quantitative CT of the lumbar spine (L1 and L2 levels) to assess bone density and strength, proton MR spectroscopy to assess BMAT (L1 and L2 levels), and MRI of the abdomen and thigh to assess body composition. Student t and Wilcoxon signed-rank tests were used to compare 24-month changes between and within groups. Regression analysis was performed to evaluate associations between body composition, vertebral bone density, strength, and BMAT. Results A total of 25 participants underwent SG (mean age, 18 years ± 2 [SD], 20 female), and 29 underwent dietary and exercise counseling without surgery (mean age, 18 years ± 3, 21 female). Body mass index (BMI) decreased by a mean of 11.9 kg/m2 ± 5.21 [SD] after 24 months in the SG group (P < .001), while it increased in the control group (mean increase, 1.49 kg/m2 ± 3.10; P = .02). Mean bone strength of the lumbar spine decreased after surgery compared with that in control subjects (mean decrease, -728 N ± 691 vs -7.24 N ± 775; P < .001). BMAT of the lumbar spine increased after SG (mean lipid-to-water ratio increase, 0.10 ± 0.13; P = .001). Changes in vertebral density and strength correlated positively with changes in BMI and body composition (R = 0.34 to R = 0.65, P = .02 to P < .001) and inversely with vertebral BMAT (R = -0.33 to R = -0.47, P = .03 to P = .001). Conclusion SG in adolescents and young adults reduced vertebral bone strength and density and increased BMAT compared with those in control participants. Clinical trial registration no. NCT02557438 © RSNA, 2023 See also the editorial by Link and Schafer in this issue.

Sensitive Immunopeptidomics by Leveraging Available Large-Scale Multi-HLA Spectral Libraries, Data-Independent Acquisition, and MS/MS Prediction.

Mass spectrometry (MS) is the state-of-the-art methodology for capturing the breadth and depth of the immunopeptidome across human leukocyte antigen (HLA) allotypes and cell types. The majority of studies in the immunopeptidomics field are discovery driven. Hence, data-dependent tandem MS (MS/MS) acquisition (DDA) is widely used, as it generates high-quality references of peptide fingerprints. However, DDA suffers from the stochastic selection of abundant ions that impairs sensitivity and reproducibility. In contrast, in data-independent acquisition (DIA), the systematic fragmentation and acquisition of all fragment ions within given isolation m/z windows yield a comprehensive map for a given sample. However, many DIA approaches commonly require generating comprehensive DDA-based spectrum libraries, which can become impractical for studying noncanonical and personalized neoantigens. Because the amount of HLA peptides eluted from biological samples such as small tissue biopsies is typically not sufficient for acquiring both meaningful DDA data necessary for generating comprehensive spectral libraries and DIA MS measurements, the implementation of DIA in the immunopeptidomics translational research domain has remained limited. We implemented a DIA immunopeptidomics workflow and assessed its sensitivity and accuracy by matching DIA data against libraries with growing complexity-from sample-specific libraries to libraries combining 2 to 40 different immunopeptidomics samples. Analyzing DIA immunopeptidomics data against a complex multi-HLA spectral library resulted in a two-fold increase in peptide identification compared with sample-specific library and in a three-fold increase compared with DDA measurements, yet with no detrimental effect on the specificity. Furthermore, we demonstrated the implementation of DIA for sensitive personalized neoantigen discovery through the analysis of DIA data with predicted MS/MS spectra of clinically relevant HLA ligands. We conclude that a comprehensive multi-HLA library for DIA approach in combination with MS/MS prediction is highly advantageous for clinical immunopeptidomics, especially when low amounts of biological samples are available.

CIITA-Transduced Glioblastoma Cells Uncover a Rich Repertoire of Clinically Relevant Tumor-Associated HLA-II Antigens.

CD4+ T cell responses are crucial for inducing and maintaining effective anticancer immunity, and the identification of human leukocyte antigen class II (HLA-II) cancer-specific epitopes is key to the development of potent cancer immunotherapies. In many tumor types, and especially in glioblastoma (GBM), HLA-II complexes are hardly ever naturally expressed. Hence, little is known about immunogenic HLA-II epitopes in GBM. With stable expression of the class II major histocompatibility complex transactivator (CIITA) coupled to a detailed and sensitive mass spectrometry-based immunopeptidomics analysis, we here uncovered a remarkable breadth of the HLA-ligandome in HROG02, HROG17, and RA GBM cell lines. The effect of CIITA expression on the induction of the HLA-II presentation machinery was striking in each of the three cell lines, and it was significantly higher compared with interferon gamma (IFNÉ£) treatment. In total, we identified 16,123 unique HLA-I peptides and 32,690 unique HLA-II peptides. In order to genuinely define the identified peptides as true HLA ligands, we carefully characterized their association with the different HLA allotypes. In addition, we identified 138 and 279 HLA-I and HLA-II ligands, respectively, most of which are novel in GBM, derived from known GBM-associated tumor antigens that have been used as source proteins for a variety of GBM vaccines. Our data further indicate that CIITA-expressing GBM cells acquired an antigen presenting cell-like phenotype as we found that they directly present external proteins as HLA-II ligands. Not only that CIITA-expressing GBM cells are attractive models for antigen discovery endeavors, but also such engineered cells have great therapeutic potential through massive presentation of a diverse antigenic repertoire.

Original research: utilization of a convolutional neural network for automated detection of lytic spinal lesions on body CTs.

OBJECTIVE: To develop, train, and test a convolutional neural network (CNN) for detection of spinal lytic lesions in chest, abdomen, and pelvis CT scans. MATERIALS AND METHODS: Cases of malignant spinal lytic lesions in CT scans were identified. Images were manually segmented for the following classes: (i) lesion, (ii) normal bone, (iii) background. If more than one lesion was on a single slice, all lesions were segmented. Images were stored as 128×128 pixel grayscale, with 10% segregated for testing. The training pipeline of the dataset included histogram equalization and data augmentation. A model was trained on Keras/Tensorflow using an 80/20 training/validation split, based on U-Net architecture. Additional testing of the model was performed on 1106 images of healthy controls. Global sensitivity measured detection of any lesion on a single image. Local sensitivity and positive predictive value (PPV) measured detection of all lesions on an image. Global specificity measured false positive rate in non-pathologic bone. RESULTS: Six hundred images were obtained for model creation. The training set consisted of 540 images, which was augmented to 20,000. The test set consisted of 60 images. Model training was performed in triplicate. Mean Dice scores were 0.61 for lytic lesion, 0.95 for normal bone, and 0.99 for background. Mean global sensitivity was 90.6%, local sensitivity was 74.0%, local PPV was 78.3%, and global specificity was 63.3%. At least one false positive lesion was noted in 28.8-44.9% of control images. CONCLUSION: A task-trained CNN showed good sensitivity in detecting spinal lytic lesions in axial CT images.

Good practices and recommendations for using and benchmarking computational metabolomics metabolite annotation tools.

BACKGROUND: Untargeted metabolomics approaches based on mass spectrometry obtain comprehensive profiles of complex biological samples. However, on average only 10% of the molecules can be annotated. This low annotation rate hampers biochemical interpretation and effective comparison of metabolomics studies. Furthermore, de novo structural characterization of mass spectral data remains a complicated and time-intensive process. Recently, the field of computational metabolomics has gained traction and novel methods have started to enable large-scale and reliable metabolite annotation. Molecular networking and machine learning-based in-silico annotation tools have been shown to greatly assist metabolite characterization in diverse fields such as clinical metabolomics and natural product discovery. AIM OF REVIEW: We highlight recent advances in computational metabolite annotation workflows with a special focus on their evaluation and comparison with other tools. Whilst the progress is substantial and promising, we also argue that inconsistencies in benchmarking different tools hamper users from selecting the most appropriate and promising method for their research. We summarize benchmarking strategies of the different tools and outline several recommendations for benchmarking and comparing novel tools. KEY SCIENTIFIC CONCEPTS OF REVIEW: This review focuses on recent advances in mass spectral library-based and machine learning-supported metabolite annotation workflows. We discuss large-scale library matching and analogue search, the current bloom of mass spectral similarity scores, and how molecular networking has changed the field. In addition, the potentials and challenges of machine learning-supported metabolite annotation workflows are highlighted. Overall, recent developments in computational metabolomics have started to fundamentally change metabolomics workflows, and we expect that as a community we will be able to overcome current method performance ambiguities and annotation bottlenecks.

Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships.

Spectral similarity is used as a proxy for structural similarity in many tandem mass spectrometry (MS/MS) based metabolomics analyses such as library matching and molecular networking. Although weaknesses in the relationship between spectral similarity scores and the true structural similarities have been described, little development of alternative scores has been undertaken. Here, we introduce Spec2Vec, a novel spectral similarity score inspired by a natural language processing algorithm-Word2Vec. Spec2Vec learns fragmental relationships within a large set of spectral data to derive abstract spectral embeddings that can be used to assess spectral similarities. Using data derived from GNPS MS/MS libraries including spectra for nearly 13,000 unique molecules, we show how Spec2Vec scores correlate better with structural similarity than cosine-based scores. We demonstrate the advantages of Spec2Vec in library matching and molecular networking. Spec2Vec is computationally more scalable allowing structural analogue searches in large databases within seconds.

Complex Multi-Stage Total Aortic and Subclavian Artery Replacement in a 9-year old boy with Loeys-Dietz-Syndrome.

BACKGROUND: Loeys-Dietz Syndrome is a rare connective tissue disorder that is associated with arterial pathologies such as aortic dissections, tortuosity and aneurysms.We present a child with Loeys-Dietz Syndrome type 2 that received total aortic and bilateral subclavian artery replacement. CASE REPORT: A 9-year old boy with Loeys-Dietz Syndrome type 2 and acute type B aortic dissection received an urgent complete thoracic and thoraco-abdominal aortic repair within three days. First, the ascending aorta and aortic root were replaced in a Tirone David and Frozen Elephant Trunk procedure. Then, the descending and supramesenteric aorta was replaced by a Dacron interposition graft with direct implantation of the celiac trunk. During the 15 months follow-up, the patient required three more surgical interventions for rapid expanding aneurysms of both subclavian arteries and the infrarenal aorta. No major adverse event nor secondary interventions occurred. Ultrasonographic and magnetic resonance imaging follow-up is continued at 6-months intervals. CONCLUSION: Children with Loeys-Dietz Syndrome may require extensive aortic repair for aortic dissection and show rapidly expanding aneurysms. Referral to a center with pediatric vascular expertise and long-term follow-up examinations are crucial.

Comparison of metal artifact reduction techniques in magnetic resonance imaging of carbon-reinforced PEEK and titanium spinal implants.

BACKGROUND: Carbon-reinforced PEEK (C-FRP) implants are non-magnetic and have increasingly been used for the fixation of spinal instabilities. PURPOSE: To compare the effect of different metal artifact reduction (MAR) techniques in magnetic resonance imaging (MRI) on titanium and C-FRP spinal implants. MATERIAL AND METHODS: Rod-pedicle screw constructs were mounted on ovine cadaver spine specimens and instrumented with either eight titanium pedicle screws or pedicle screws made of C-FRP and marked with an ultrathin titanium shell. MR scans were performed of each configuration on a 3-T scanner. MR sequences included transaxial conventional T1-weighted turbo spin echo (TSE) sequences, T2-weighted TSE, and short-tau inversion recovery (STIR) sequences and two different MAR-techniques: high-bandwidth (HB) and view-angle-tilting (VAT) with slice encoding for metal artifact correction (SEMAC). Metal artifact degree was assessed by qualitative and quantitative measures. RESULTS: There was a much stronger effect on artifact reduction with using C-FRP implants compared to using specific MRI MAR-techniques (screw shank: P < 0.001; screw tulip: P < 0.001; rod: P < 0.001). VAT-SEMAC sequences were able to reduce screw-related signal loss artifacts in constructs with titanium screws to a certain degree. Constructs with C-FRP screws showed less artifact-related implant diameter amplification when compared to constructs with titanium screws (P < 0.001). CONCLUSION: Constructs with C-FRP screws are associated with significantly less artifacts compared to constructs with titanium screws including dedicated MAR techniques. Artifact-reducing sequences are able to reduce implant-related artifacts. This effect is stronger in constructs with titanium screws than in constructs with C-FRP screws.

AI MSK clinical applications: spine imaging.

Recent investigations have focused on the clinical application of artificial intelligence (AI) for tasks specifically addressing the musculoskeletal imaging routine. Several AI applications have been dedicated to optimizing the radiology value chain in spine imaging, independent from modality or specific application. This review aims to summarize the status quo and future perspective regarding utilization of AI for spine imaging. First, the basics of AI concepts are clarified. Second, the different tasks and use cases for AI applications in spine imaging are discussed and illustrated by examples. Finally, the authors of this review present their personal perception of AI in daily imaging and discuss future chances and challenges that come along with AI-based solutions.

A shot for the US economy.

While previous literature examines the effects of increasing COVID-19 incidences and fatality rates on economic activity, the impact of vaccination roll-outs on public health and the economy is not yet well understood. We examine the effect of a vaccination shock in the United States on various pandemic and economic indicators. By employing a BVAR model to overcome the short data sample, we show that an increase in vaccinations is not only associated with declining incidences, reproduction and fatality rates, but also increases mobility, which dampens the effect on public health indicators in the medium term. With respect to the economy, a vaccination shock is associated with lower unemployment, higher GDP growth and also reduces uncertainty in financial markets.

Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches.

Covering: up to the end of 2020Recently introduced computational metabolome mining tools have started to positively impact the chemical and biological interpretation of untargeted metabolomics analyses. We believe that these current advances make it possible to start decomposing complex metabolite mixtures into substructure and chemical class information, thereby supporting pivotal tasks in metabolomics analysis including metabolite annotation, the comparison of metabolic profiles, and network analyses. In this review, we highlight and explain key tools and emerging strategies covering 2015 up to the end of 2020. The majority of these tools aim at processing and analyzing liquid chromatography coupled to mass spectrometry fragmentation data. We start with defining what substructures are, how they relate to molecular fingerprints, and how recognizing them helps to decompose complex mixtures. We continue with chemical classes that are based on the presence or absence of particular molecular scaffolds and/or functional groups and are thus intrinsically related to substructures. We discuss novel tools to mine substructures, annotate chemical compound classes, and create mass spectral networks from metabolomics data and demonstrate them using two case studies. We also review and speculate about the opportunities that NMR spectroscopy-based metabolome mining of complex metabolite mixtures offers to discover substructures and chemical classes. Finally, we will describe the main benefits and limitations of the current tools and strategies that rely on them, and our vision on how this exciting field can develop toward repository-scale-sized metabolomics analyses. Complementary sources of structural information from genomics analyses and well-curated taxonomic records are also discussed. Many research fields such as natural products discovery, pharmacokinetic and drug metabolism studies, and environmental metabolomics increasingly rely on untargeted metabolomics to gain biochemical and biological insights. The here described technical advances will benefit all those metabolomics disciplines by transforming spectral data into knowledge that can answer biological questions.

Genome-wide C-SWAT library for high-throughput yeast genome tagging.

Here we describe a C-SWAT library for high-throughput tagging of Saccharomyces cerevisiae open reading frames (ORFs). In 5,661 strains, we inserted an acceptor module after each ORF that can be efficiently replaced with tags or regulatory elements. We validated the library with targeted sequencing and tagged the proteome with bright fluorescent proteins to quantify the effect of heterologous transcription terminators on protein expression and to localize previously undetected proteins.