AI-accelerated protein-ligand docking for SARS-CoV-2 is 100-fold faster with no significant change in detection.

Protein-ligand docking is a computational method for identifying drug leads. The method is capable of narrowing a vast library of compounds down to a tractable size for downstream simulation or experimental testing and is widely used in drug discovery. While there has been progress in accelerating scoring of compounds with artificial intelligence, few works have bridged these successes back to the virtual screening community in terms of utility and forward-looking development. We demonstrate the power of high-speed ML models by scoring 1 billion molecules in under a day (50 k predictions per GPU seconds). We showcase a workflow for docking utilizing surrogate AI-based models as a pre-filter to a standard docking workflow. Our workflow is ten times faster at screening a library of compounds than the standard technique, with an error rate less than 0.01% of detecting the underlying best scoring 0.1% of compounds. Our analysis of the speedup explains that another order of magnitude speedup must come from model accuracy rather than computing speed. In order to drive another order of magnitude of acceleration, we share a benchmark dataset consisting of 200 million 3D complex structures and 2D structure scores across a consistent set of 13 million "in-stock" molecules over 15 receptors, or binding sites, across the SARS-CoV-2 proteome. We believe this is strong evidence for the community to begin focusing on improving the accuracy of surrogate models to improve the ability to screen massive compound libraries 100 × or even 1000 × faster than current techniques and reduce missing top hits. The technique outlined aims to be a fast drop-in replacement for docking for screening billion-scale molecular libraries.

Introducing the Bacterial and Viral Bioinformatics Resource Center (BV-BRC): a resource combining PATRIC, IRD and ViPR.

The National Institute of Allergy and Infectious Diseases (NIAID) established the Bioinformatics Resource Center (BRC) program to assist researchers with analyzing the growing body of genome sequence and other omics-related data. In this report, we describe the merger of the PAThosystems Resource Integration Center (PATRIC), the Influenza Research Database (IRD) and the Virus Pathogen Database and Analysis Resource (ViPR) BRCs to form the Bacterial and Viral Bioinformatics Resource Center (BV-BRC) https://www.bv-brc.org/. The combined BV-BRC leverages the functionality of the bacterial and viral resources to provide a unified data model, enhanced web-based visualization and analysis tools, bioinformatics services, and a powerful suite of command line tools that benefit the bacterial and viral research communities.

A cross-study analysis of drug response prediction in cancer cell lines.

To enable personalized cancer treatment, machine learning models have been developed to predict drug response as a function of tumor and drug features. However, most algorithm development efforts have relied on cross-validation within a single study to assess model accuracy. While an essential first step, cross-validation within a biological data set typically provides an overly optimistic estimate of the prediction performance on independent test sets. To provide a more rigorous assessment of model generalizability between different studies, we use machine learning to analyze five publicly available cell line-based data sets: National Cancer Institute 60, ancer Therapeutics Response Portal (CTRP), Genomics of Drug Sensitivity in Cancer, Cancer Cell Line Encyclopedia and Genentech Cell Line Screening Initiative (gCSI). Based on observed experimental variability across studies, we explore estimates of prediction upper bounds. We report performance results of a variety of machine learning models, with a multitasking deep neural network achieving the best cross-study generalizability. By multiple measures, models trained on CTRP yield the most accurate predictions on the remaining testing data, and gCSI is the most predictable among the cell line data sets included in this study. With these experiments and further simulations on partial data, two lessons emerge: (1) differences in viability assays can limit model generalizability across studies and (2) drug diversity, more than tumor diversity, is crucial for raising model generalizability in preclinical screening.

Pain Relief After Selective Nerve Root Block as a Predictor of Postoperative Functional Outcome in Patients with Degenerative Lumbar Spinal Stenosis Patients Undergoing Decompressive Surgery.

STUDY DESIGN: Retrospective study of data collected prospectively. OBJECTIVE: To investigate changes in the degree of lower leg radiating pain (LLRP) after selective nerve root block (SNRB) and to evaluate associations of this change with postoperative improvements in symptom severity, functional outcomes, and quality of life. SUMMARY OF BACKGROUND DATA: SNRB is routinely performed as an initial treatment for lumbar foraminal or lateral recess stenosis with LLRP. The degree of improvement after SNRB has been suggested to predict the improvement in postoperative pain and functional outcomes. However, there have been no studies on the predictive value of this parameter. METHODS: We enrolled 60 patients who underwent SNRB followed by decompressive surgery. They were divided into three groups. The degree of improvement was evaluated as a percentage of the pre-injection values. Functional outcomes of the spine were assessed using the Oswestry Disability Index (ODI) and Roland-Morris Disability Questionnaire (RMDQ). Quality of life was assessed using the 36-item Short Form Survey (SF-36) physical component score (PCS) and mental component score (MCS). The degree of LLRP was measured preoperatively and at 6, 12, and 24 months after surgery. These functional outcomes were evaluated preoperatively and at 12 and 24 months after surgery. RESULTS: The improvement in LLRP in the short term (6 hours after SNRB) was found to be statistically significantly associated with the improvement in LLRP at 12 months after SNRB (P = 0.044, correlation coefficient = 0.261). No relationship between pain improvement after SNRB and functional outcome was identified. CONCLUSION: The degree of improvement in symptoms 6 hours after SNRB can predict the degree of improvement in LLRP at 12 months after surgery. However, symptomatic improvement after SNRB does not predict postoperative functional outcome or quality of life.Level of Evidence: 4.

A genomic data resource for predicting antimicrobial resistance from laboratory-derived antimicrobial susceptibility phenotypes.

Antimicrobial resistance (AMR) is a major global health threat that affects millions of people each year. Funding agencies worldwide and the global research community have expended considerable capital and effort tracking the evolution and spread of AMR by isolating and sequencing bacterial strains and performing antimicrobial susceptibility testing (AST). For the last several years, we have been capturing these efforts by curating data from the literature and data resources and building a set of assembled bacterial genome sequences that are paired with laboratory-derived AST data. This collection currently contains AST data for over 67 000 genomes encompassing approximately 40 genera and over 100 species. In this paper, we describe the characteristics of this collection, highlighting areas where sampling is comparatively deep or shallow, and showing areas where attention is needed from the research community to improve sampling and tracking efforts. In addition to using the data to track the evolution and spread of AMR, it also serves as a useful starting point for building machine learning models for predicting AMR phenotypes. We demonstrate this by describing two machine learning models that are built from the entire dataset to show where the predictive power is comparatively high or low. This AMR metadata collection is freely available and maintained on the Bacterial and Viral Bioinformatics Center (BV-BRC) FTP site ftp://ftp.bvbrc.org/RELEASE_NOTES/PATRIC_genomes_AMR.txt.

Converting tabular data into images for deep learning with convolutional neural networks.

Convolutional neural networks (CNNs) have been successfully used in many applications where important information about data is embedded in the order of features, such as speech and imaging. However, most tabular data do not assume a spatial relationship between features, and thus are unsuitable for modeling using CNNs. To meet this challenge, we develop a novel algorithm, image generator for tabular data (IGTD), to transform tabular data into images by assigning features to pixel positions so that similar features are close to each other in the image. The algorithm searches for an optimized assignment by minimizing the difference between the ranking of distances between features and the ranking of distances between their assigned pixels in the image. We apply IGTD to transform gene expression profiles of cancer cell lines (CCLs) and molecular descriptors of drugs into their respective image representations. Compared with existing transformation methods, IGTD generates compact image representations with better preservation of feature neighborhood structure. Evaluated on benchmark drug screening datasets, CNNs trained on IGTD image representations of CCLs and drugs exhibit a better performance of predicting anti-cancer drug response than both CNNs trained on alternative image representations and prediction models trained on the original tabular data.

Learning curves for drug response prediction in cancer cell lines.

BACKGROUND: Motivated by the size and availability of cell line drug sensitivity data, researchers have been developing machine learning (ML) models for predicting drug response to advance cancer treatment. As drug sensitivity studies continue generating drug response data, a common question is whether the generalization performance of existing prediction models can be further improved with more training data. METHODS: We utilize empirical learning curves for evaluating and comparing the data scaling properties of two neural networks (NNs) and two gradient boosting decision tree (GBDT) models trained on four cell line drug screening datasets. The learning curves are accurately fitted to a power law model, providing a framework for assessing the data scaling behavior of these models. RESULTS: The curves demonstrate that no single model dominates in terms of prediction performance across all datasets and training sizes, thus suggesting that the actual shape of these curves depends on the unique pair of an ML model and a dataset. The multi-input NN (mNN), in which gene expressions of cancer cells and molecular drug descriptors are input into separate subnetworks, outperforms a single-input NN (sNN), where the cell and drug features are concatenated for the input layer. In contrast, a GBDT with hyperparameter tuning exhibits superior performance as compared with both NNs at the lower range of training set sizes for two of the tested datasets, whereas the mNN consistently performs better at the higher range of training sizes. Moreover, the trajectory of the curves suggests that increasing the sample size is expected to further improve prediction scores of both NNs. These observations demonstrate the benefit of using learning curves to evaluate prediction models, providing a broader perspective on the overall data scaling characteristics. CONCLUSIONS: A fitted power law learning curve provides a forward-looking metric for analyzing prediction performance and can serve as a co-design tool to guide experimental biologists and computational scientists in the design of future experiments in prospective research studies.

Comparison of Minimal Invasive Versus Biportal Endoscopic Transforaminal Lumbar Interbody Fusion for Single-level Lumbar Disease.

STUDY DESIGN: Retrospective study. OBJECTIVE: The authors aimed to compare the clinical outcomes of biportal endoscopic transforaminal lumbar interbody fusion (BE-TLIF) with those of minimally invasive transforaminal lumbar interbody fusion (MI-TLIF) using a microscope. SUMMARY OF BACKGROUND DATA: Lumbar spinal fusion has been widely performed for various lumbar spinal pathologies. Minimally invasive transforaminal interbody fusion using a tubular retractor under a microscope is a method of achieving fusion while reducing soft tissue injury. Recently, several studies have reported minimally invasive techniques for lumbar discectomy, decompression, and interbody fusion using biportal endoscopic spinal surgery. MATERIALS AND METHODS: This retrospective study included 87 patients who underwent single-level TLIF for degenerative or isthmic spondylolisthesis between 2015 and 2018. Thirty-two and 55 patients underwent BE-TLIF (group A) and MI-TLIF (group B), respectively. Visual Analogue Scale scores of the back and leg and Oswestry Disability Index were collected perioperatively.Further, data regarding perioperative complications, including length of hospital stay, time to ambulation, and fusion rate, were collected. RESULTS: The Visual Analogue Scale score at 2 weeks and 2 months postoperatively was significantly lower in group A (P=0.001). All other clinical scores showed improvement with no significant difference between the 2 groups (P>0.05). The difference in the fusion rates between group A (93.7%) and group B (92.7%) were not significant (P=0.43). CONCLUSIONS: Because BE-TLIF yieldeds lesser early postoperative back pain than did MI-TLIF, it may allow early ambulation and a shorter hospitalization period. BE-TLIF may be a viable alternative to MI-TLIF in patients with degenerative or isthmic spondylolisthesis with superior clinical results in the early postoperative period.

Learning Curve and Clinical Outcome of Biportal Endoscopic-Assisted Lumbar Interbody Fusion.

Interbody fusion is a common surgical technique for diseases of the lumbar spine. Biportal endoscopic-assisted lumbar interbody fusion (BE-LIF) is a novel minimally invasive technique that has a long learning curve, which can be a barrier for surgeons. Therefore, we analyzed the learning curve in terms of operative time and evaluated the outcomes of BE-LIF. A retrospective study of fifty-seven consecutive patients who underwent BE-LIF for degenerative lumbar disease by a single surgeon from January 2017 to December 2018 was performed. Fifty patients underwent a single-level procedure, and 7 underwent surgery at two levels. The mean follow-up period was 24 months (range, 14-38). Total operative time, postoperative drainage volume, time to ambulation, and complications were analyzed. Clinical outcome was measured using the Oswestry Disability Index (ODI), Visual Analog Scale (VAS) score for back and leg pain, and modified Macnab criteria. The learning curve was evaluated by a nonparametric regression locally weighted scatterplot smoothing curve. Cases before the stable point on the curve were designated as group A, and those after the stable point were designated group B. Operative time decreased as the number of cases increased. A stable point was noticed on the 400th day and the 34th case after the first BE-LIF was performed. All cases showed improved ODI and VAS scores at the final follow-up. Overall mean operative time was 171.74 ± 35.1 min. Mean operative time was significantly lower in group B (139.7 ± 11.6 min) compared to group A (193.4 ± 28.3 min). Time to ambulation was significantly lower in group B compared to group A. VAS and ODI scores did not differ between the two groups. BE-LIF is an effective minimally invasive technique for lumbar degenerative disease. In our case series, this technique required approximately 34 cases to reach an adequate performance level.

Ensemble transfer learning for the prediction of anti-cancer drug response.

Transfer learning, which transfers patterns learned on a source dataset to a related target dataset for constructing prediction models, has been shown effective in many applications. In this paper, we investigate whether transfer learning can be used to improve the performance of anti-cancer drug response prediction models. Previous transfer learning studies for drug response prediction focused on building models to predict the response of tumor cells to a specific drug treatment. We target the more challenging task of building general prediction models that can make predictions for both new tumor cells and new drugs. Uniquely, we investigate the power of transfer learning for three drug response prediction applications including drug repurposing, precision oncology, and new drug development, through different data partition schemes in cross-validation. We extend the classic transfer learning framework through ensemble and demonstrate its general utility with three representative prediction algorithms including a gradient boosting model and two deep neural networks. The ensemble transfer learning framework is tested on benchmark in vitro drug screening datasets. The results demonstrate that our framework broadly improves the prediction performance in all three drug response prediction applications with all three prediction algorithms.

Enhanced Co-Expression Extrapolation (COXEN) Gene Selection Method for Building Anti-Cancer Drug Response Prediction Models.

The co-expression extrapolation (COXEN) method has been successfully used in multiple studies to select genes for predicting the response of tumor cells to a specific drug treatment. Here, we enhance the COXEN method to select genes that are predictive of the efficacies of multiple drugs for building general drug response prediction models that are not specific to a particular drug. The enhanced COXEN method first ranks the genes according to their prediction power for each individual drug and then takes a union of top predictive genes of all the drugs, among which the algorithm further selects genes whose co-expression patterns are well preserved between cancer cases for building prediction models. We apply the proposed method on benchmark in vitro drug screening datasets and compare the performance of prediction models built based on the genes selected by the enhanced COXEN method to that of models built on genes selected by the original COXEN method and randomly picked genes. Models built with the enhanced COXEN method always present a statistically significantly improved prediction performance (adjusted p-value ≤ 0.05). Our results demonstrate the enhanced COXEN method can dramatically increase the power of gene expression data for predicting drug response.

Effectiveness of Gelatin-Thrombin Matrix Sealants (Floseal®) on Postoperative Spinal Epidural Hematoma during Single-Level Lumbar Decompression Using Biportal Endoscopic Spine Surgery: Clinical and Magnetic Resonance Image Study.

BACKGROUND: Symptomatic postoperative spinal epidural hematoma (PSEH) is a devastating complication that could develop after lumbar decompression surgery. PSEH can also develop after biportal endoscopic spine surgery (BESS), one of the recently introduced minimally invasive spine surgery techniques. Gelatin-thrombin matrix sealant (GTMS) is commonly used to prevent PSEH. This study aimed at analyzing the clinical and radiological effects of GTMS use during BESS. METHODS: A total of 206 patients with spinal stenosis who underwent decompression by BESS through a posterior interlaminar approach from October 2015 to September 2018 were enrolled in this study. Postoperative magnetic resonance imaging (MRI) was performed in all patients for evaluation of PSEH. Patients in whom GTMS was not used during surgery were assigned to Group A, and those in whom GTMS was used were classified as Group B. In the clinical evaluation, the visual analog scale (VAS) of the leg and back, Oswestry Disability Index (ODI), and modified MacNab criteria were used. The incidence rate and degree of dural compression of PSEH on postoperative MRI were measured. RESULTS: The average age of the patients was 68.1 ± 11.2 (42-89) years. The overall incidence rate of PSEH was 20.9% (43/206). The incidence rates in Groups A and B were 26.4% and 13.6%, respectively, showing a significant difference (p = 0.023). The VAS-leg and ODI improvement was significantly different depending on the intraoperative use of GTMS. However, there was no statistically significant difference between the two groups in terms of the VAS-back improvement. Groups A and B showed "good" and "excellent" rates according to the modified MacNab criteria in 79.4% and 87.6% of patients, respectively, showing statistically significant difference (p = 0.049). In Group A, two patients underwent revision surgery due to PSEH, while none in Group B had such event. CONCLUSION: Intraoperative use of GTMS during BESS may be related to reduction in the occurrence rate of PSEH. Specifically, patients with GTMS appliance showed marked decrease in the occurrence of PSEH and had better clinical outcomes.

The PATRIC Bioinformatics Resource Center: expanding data and analysis capabilities.

The PathoSystems Resource Integration Center (PATRIC) is the bacterial Bioinformatics Resource Center funded by the National Institute of Allergy and Infectious Diseases (https://www.patricbrc.org). PATRIC supports bioinformatic analyses of all bacteria with a special emphasis on pathogens, offering a rich comparative analysis environment that provides users with access to over 250 000 uniformly annotated and publicly available genomes with curated metadata. PATRIC offers web-based visualization and comparative analysis tools, a private workspace in which users can analyze their own data in the context of the public collections, services that streamline complex bioinformatic workflows and command-line tools for bulk data analysis. Over the past several years, as genomic and other omics-related experiments have become more cost-effective and widespread, we have observed considerable growth in the usage of and demand for easy-to-use, publicly available bioinformatic tools and services. Here we report the recent updates to the PATRIC resource, including new web-based comparative analysis tools, eight new services and the release of a command-line interface to access, query and analyze data.

Outcomes of Combined Neck and Trochanter Fractures of the Femur Treated with Cephallomedullary Nail in Elderly.

PURPOSE: Proximal femur fractures are classified into intracapsular neck fractures and extracapsular trochanteric fractures, and several related treatment recommendations in elderly patients have already been introduced. Importantly, we have observed cases of combined intra and extracapsular fractures (i.e., ipsilateral neck and trochanter fractures). The purpose of this study is to report the outcomes of combined neck and trochanter fractures of the femur treated with cephalomedullary nail (CMN) in elderly patients. MATERIALS AND METHODS: From January 2010 to December 2014, 410 patients with proximal femoral fractures were fixed using CMN; among this group, 37 patients with combined neck and trochanter fractures were identified. Two of these patients died fewer than three months after injury and another two did not return for follow-up. Thirty-three patients were included and reviewed retrospectively in this study. RESULTS: All patients were injured by simple fall. Bone union was obtained in 28 of 33 patients. Of the five patients who failed treatment, three experienced implant penetration through head (cut-through and cut-out), one had breakage of CMN and the last one had a loosening of internal fixation device with persistent non-union at final follow-up. The former four patients underwent hip replacement surgery and the latter refused surgery because he had low demand in daily life and many medical problems. CONCLUSION: Eighty-five percent of elderly patients with combined neck and trochanter fractures of the femur treated with CMN achieved bone union; these complex fractures require more accurate reduction than usual extra-articular intertrochanteric fractures.

Deletion at 2q14.3 is associated with worse response to TNF-α blockers in patients with rheumatoid arthritis.

BACKGROUND: Structural variations such as copy number variations (CNVs) have a functional impact on various human traits. This study profiled genome-wide CNVs in Korean patients with rheumatoid arthritis (RA) to investigate the efficacy of treatment with TNF-α blockers. METHODS: A total of 357 Korean patients with RA were examined for the efficacy of TNF-α blocker treatment. Disease activity indexes were measured at baseline and 6 months after the treatment. The patients were classified as responders and non-responders based on the change in disease activity indexes according to the EULAR response criteria. CNVs in the same patients were profiled using fluorescence signal intensity data generated by a genome-wide SNP array. The association of CNVs with response to TNF-α blockers was analyzed by multivariate logistic regression accounting for genetic background and clinical factors including body mass index, gender, baseline disease activity, TNF-α blocker used, and methotrexate treatment. RESULTS: The study subjects varied in their responses to TNF-α blockers and had 286 common CNVs in autosomes. We identified that the 3.8-kb deletion at 2q14.3 in 5% of the subjects was associated with response to TNF-α blockers (1.37 × 10- 5 ≤ P ≤ 4.07 × 10- 4) at a false discovery rate threshold of 5%. The deletion in the identified CNV was significantly more frequent in the non-responders than in the responders, indicating worse response to TNF-α blockers in the deletion carriers. The 3.8-kb deletion at 2q14.3 is located in an intergenic region with the binding sites of two transcription factors, MAFF and MAFK. CONCLUSIONS: This study obtained the CNV landscape of Korean patients with RA and identified the common regional deletion associated with poor response to treatment with TNF-α blockers.

PATRIC as a unique resource for studying antimicrobial resistance.

The Pathosystems Resource Integration Center (PATRIC, www.patricbrc.org) is designed to provide researchers with the tools and services that they need to perform genomic and other 'omic' data analyses. In response to mounting concern over antimicrobial resistance (AMR), the PATRIC team has been developing new tools that help researchers understand AMR and its genetic determinants. To support comparative analyses, we have added AMR phenotype data to over 15 000 genomes in the PATRIC database, often assembling genomes from reads in public archives and collecting their associated AMR panel data from the literature to augment the collection. We have also been using this collection of AMR metadata to build machine learning-based classifiers that can predict the AMR phenotypes and the genomic regions associated with resistance for genomes being submitted to the annotation service. Likewise, we have undertaken a large AMR protein annotation effort by manually curating data from the literature and public repositories. This collection of 7370 AMR reference proteins, which contains many protein annotations (functional roles) that are unique to PATRIC and RAST, has been manually curated so that it projects stably across genomes. The collection currently projects to 1 610 744 proteins in the PATRIC database. Finally, the PATRIC Web site has been expanded to enable AMR-based custom page views so that researchers can easily explore AMR data and design experiments based on whole genomes or individual genes.

Genomic Analysis of Metastatic Solid Tumors in Veterans: Findings From the VHA National Precision Oncology Program.

PURPOSE: The Veterans Health Administration (VHA) is the largest cancer care provider in the United States, with the added challenge of serving more than twice the percentage of patients with cancer in rural areas than the national average. The VHA established the National Precision Oncology Program in 2016 to implement and standardize the practice of precision oncology across the diverse VHA system. METHODS: Tumor or peripheral blood specimens from veterans with advanced solid tumors who were eligible for treatment were submitted for next-generation sequencing (NGS) at two commercial laboratories. Annotated results were generated by the laboratories and independently using IBM Watson for Genomics. Levels-of-evidence treatment recommendations were based on OncoKB criteria. RESULTS: From July 2016 to June 2018, 3,698 samples from 72 VHA facilities were submitted for NGS testing, of which 3,182 samples (86%) were successfully sequenced. Most samples came from men with lung, prostate, and colorectal cancers. Thirty-four percent of samples were from patients who lived in a rural area. TP53, ATM, and KRAS were among the most commonly mutated genes. Approximately 70% of samples had at least one actionable mutation, with clinical trials identified as the recommended option in more than 50%. Mutations in genes associated with a neuroendocrine prostate cancer phenotype were expressed at increased frequency among veterans than in the general population. The most frequent therapies prescribed in response to NGS testing were immune checkpoint inhibitors, EGFR kinase inhibitors, and PARP inhibitors. CONCLUSION: Clinical implementation of precision oncology is feasible across the VHA health care system, including rural sites. Veterans have unique occupational exposures that might inform the nature of the mutational signatures identified here. Importantly, these results underscore the importance of increasing clinical trial availability to veterans.

Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae.

Antimicrobial resistant infections are a serious public health threat worldwide. Whole genome sequencing approaches to rapidly identify pathogens and predict antibiotic resistance phenotypes are becoming more feasible and may offer a way to reduce clinical test turnaround times compared to conventional culture-based methods, and in turn, improve patient outcomes. In this study, we use whole genome sequence data from 1668 clinical isolates of Klebsiella pneumoniae to develop a XGBoost-based machine learning model that accurately predicts minimum inhibitory concentrations (MICs) for 20 antibiotics. The overall accuracy of the model, within ±1 two-fold dilution factor, is 92%. Individual accuracies are ≥90% for 15/20 antibiotics. We show that the MICs predicted by the model correlate with known antimicrobial resistance genes. Importantly, the genome-wide approach described in this study offers a way to predict MICs for isolates without knowledge of the underlying gene content. This study shows that machine learning can be used to build a complete in silico MIC prediction panel for K. pneumoniae and provides a framework for building MIC prediction models for other pathogenic bacteria.

Assembly, Annotation, and Comparative Genomics in PATRIC, the All Bacterial Bioinformatics Resource Center.

In the "big data" era, research biologists are faced with analyzing new types that usually require some level of computational expertise. A number of programs and pipelines exist, but acquiring the expertise to run them, and then understanding the output can be a challenge.The Pathosystems Resource Integration Center (PATRIC, www.patricbrc.org ) has created an end-to-end analysis platform that allows researchers to take their raw reads, assemble a genome, annotate it, and then use a suite of user-friendly tools to compare it to any public data that is available in the repository. With close to 113,000 bacterial and more than 1000 archaeal genomes, PATRIC creates a unique research experience with "virtual integration" of private and public data. PATRIC contains many diverse tools and functionalities to explore both genome-scale and gene expression data, but the main focus of this chapter is on assembly, annotation, and the downstream comparative analysis functionality that is freely available in the resource.