The quality of automated gene prediction in microbial organisms has improved steadily over the past decade, but there is still room for improvement. Increasing the number of correct identifications, both of genes and of the translation initiation sites for each gene, and reducing the overall number of false positives, are all desirable goals. With our years of experience in manually curating genomes for the Joint Genome Institute, we developed a new gene prediction algorithm called Prodigal (PROkaryotic DYnamic programming Gene-finding ALgorithm). With Prodigal, we focused specifically on the three goals of improved gene structure prediction, improved translation initiation site recognition, and reduced false positives. We compared the results of Prodigal to existing gene-finding methods to demonstrate that it met each of these objectives. We built a fast, lightweight, open source gene prediction program called Prodigal http://compbio.ornl.gov/prodigal/
 . Prodigal achieved good results compared to existing methods, and we believe it will be a valuable asset to automated microbial annotation pipelines.

/pdf/prodigal-prokaryotic-gene-recognition-and-translation-4my2za2vic.pdf

Prodigal: prokaryotic gene recognition and translation initiation site identification

The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.

Integrative analysis of 111 reference human epigenomes

Evolution of Protein Molecules

Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ

Mass spectrometry (MS) coupled with online separation methods is commonly applied for differential and quantitative profiling of biological samples in metabolomic as well as proteomic research. Such approaches are used for systems biology, functional genomics, and biomarker discovery, among others. An ongoing challenge of these molecular profiling approaches, however, is the development of better data processing methods. Here we introduce a new generation of a popular open-source data processing toolbox, MZmine 2. A key concept of the MZmine 2 software design is the strict separation of core functionality and data processing modules, with emphasis on easy usability and support for high-resolution spectra processing. Data processing modules take advantage of embedded visualization tools, allowing for immediate previews of parameter settings. Newly introduced functionality includes the identification of peaks using online databases, MSn data support, improved isotope pattern support, scatter plot visualization, and a new method for peak list alignment based on the random sample consensus (RANSAC) algorithm. The performance of the RANSAC alignment was evaluated using synthetic datasets as well as actual experimental data, and the results were compared to those obtained using other alignment algorithms. MZmine 2 is freely available under a GNU GPL license and can be obtained from the project website at: http://mzmine.sourceforge.net/
 . The current version of MZmine 2 is suitable for processing large batches of data and has been applied to both targeted and non-targeted metabolomic analyses.

/pdf/mzmine-2-modular-framework-for-processing-visualizing-and-3dr0n6iwn7.pdf

MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data

The widely used model for evolutionary relationships is a bifurcating tree with all taxa/observations placed at the leaves. This is not appropriate if the taxa have been densely sampled across evolutionary time and may be in a direct ancestral relationship, or if there is not enough information to fully resolve all the branching points in the evolutionary tree. In this article, we present a fast distance-based agglomeration method called family-joining (FJ) for constructing so-called generally labeled trees in which taxa may be placed at internal vertices and the tree may contain polytomies. FJ constructs such trees on the basis of pairwise distances and a distance threshold. We tested three methods for threshold selection, FJ-AIC, FJ-BIC, and FJ-CV, which minimize Akaike information criterion, Bayesian information criterion, and cross-validation error, respectively. When compared with related methods on simulated data, FJ-BIC was among the best at reconstructing the correct tree across a wide range of simulation scenarios. FJ-BIC was applied to HIV sequences sampled from individuals involved in a known transmission chain. The FJ-BIC tree was found to be compatible with almost all transmission events. On average, internal branches in the FJ-BIC tree have higher bootstrap support than branches in the leaf-labeled bifurcating tree constructed using RAxML. 36% and 25% of the internal branches in the FJ-BIC tree and RAxML tree, respectively, have bootstrap support greater than 70%. To the best of our knowledge the method presented here is the first attempt at modeling evolutionary relationships using generally labeled trees.

/pdf/family-joining-a-fast-distance-based-method-for-constructing-3qbsxiy8kd.pdf

Family-joining: A fast distance-based method for constructing generally labeled trees

The HIV epidemic in Eastern Europe and Russia is large and not well-controlled. To describe the more recent molecular epidemiology of HIV-1, transmitted drug resistance, and the relationship between the epidemics in this region, we sequenced the protease and reverse transcriptase genes of HIV-1 from 812 people living with HIV from Ukraine (n = 191), Georgia (n = 201), and Russia (n = 420) before the initiation of antiretroviral therapy. In 190 Ukrainian patients, the integrase gene sequence was also determined. The most reported route of transmission was heterosexual contact, followed by intravenous drug use, and men having sex with men (MSM). Several pre-existing drug resistance mutations were found against non-nucleoside reverse transcriptase inhibitors (RTIs) (n = 103), protease inhibitors (n = 11), and nucleoside analogue RTIs (n = 12), mostly polymorphic mutations or revertants. In the integrase gene, four strains with accessory integrase strand transfer inhibitor mutations were identified. Sub-subtype A6 caused most of the infections (713/812; 87.8%) in all three countries, including in MSM. In contrast to earlier studies, no clear clusters related to the route of transmission were identified, indicating that, within the region, the exchange of viruses among the different risk groups may occur more often than earlier reported.

/pdf/molecular-epidemiology-of-hiv-1-in-eastern-europe-and-russia-k0harbip.pdf

Molecular Epidemiology of HIV-1 in Eastern Europe and Russia

http://www.thelancet.com/article/S2352396422002961/pdf

Predicting the functional effects of voltage-gated potassium channel missense variants with multi-task learning

For many diseases it is necessary to gather large cohorts of patients with the disease in order to have enough power to discover the important factors. In this setting, it is very important to preserve the privacy of each patient and ideally remove the necessity to gather all data in one place. Examples include genomic research of cancer, infectious diseases or Alzheimer’s. This problem leads us to develop privacy preserving machine learning algorithms. So far in the literature there are studies addressing the calculation of a specific function privately with lack of generality or utilizing computationally expensive encryption to preserve the privacy, which slows down the computation significantly. In this study, we propose a framework utilizing randomized encoding in which four basic arithmetic operations (addition, subtraction, multiplication and division) can be performed, in order to allow the calculation of machine learning algorithms involving one type of these operations privately. Among the suitable machine learning algorithms, we apply the oligo kernel and the radial basis function kernel to the coreceptor usage prediction problem of HIV by employing the framework to calculate the kernel functions. The results show that we do not sacrifice the performance of the algorithms for privacy in terms of F1-score and AUROC. Furthermore, the execution time of the framework in the experiments of the oligo kernel is comparable with the non-private version of the computation. Our framework in the experiments of radial basis function kernel is also way faster than the existing approaches utilizing integer vector homomorphic encryption and consequently homomorphic encryption based solutions, which indicates that our approach has a potential for application to many other diseases and data types.

A Framework with Randomized Encoding for a Fast Privacy Preserving Calculation of Non-linear Kernels for Machine Learning Applications in Precision Medicine

Machine learning methods are employed in many areas, such as medical data research, for their efficient and powerful data mining ability. However, submitting unprotected data to a third party, which attempts to train a machine learning model, may suffer from data leakage and privacy violation when the third party is compromised by an adversary. Hence, designing a protocol to execute encrypted computation is inevitably indispensable. In order to address this problem, we propose protocols based on secure multi-party computation to train a support vector machine model privately. Utilizing the semi-honest adversary model and oblivious transfer, the proposed protocols enable the training of a non-linear support vector machine on the combined data from various sources without sacrificing the privacy of individuals. The protocols are applied to train a support vector machine model with the radial basis function kernel on HIV sequence data to predict the efficacy of a certain antiviral drug, which only works if the viruses can only use the human CCR5 coreceptor for cell entry. Benchmarked on synthesized data with 10 data sources that consist of randomly generated integers, containing 100 labeled samples each, the protocol has consumed online time 2991.386/166.912 ms on average in arithmetic/boolean circuits, respectively. The cross-validation has reached 0.5819 F1-score on average on training data with the optimized parameters, which have reached 0.7058 F1-score afterwards on testing data set, which consists of protein sequence of CCR5 and its subtypes. The complete training and testing process on the real data, which contains in total 766 samples having 924 features after encoding, has consumed 43.75/15.84 seconds on average using arithmetic/boolean circuits, respectively, which shows the effectiveness and efficiency of our protocols compared to some of the existing studies in the literature.

Nico Pfeifer

Papers

Family-joining: A fast distance-based method for constructing generally labeled trees

Molecular Epidemiology of HIV-1 in Eastern Europe and Russia

Predicting the functional effects of voltage-gated potassium channel missense variants with multi-task learning

A Framework with Randomized Encoding for a Fast Privacy Preserving Calculation of Non-linear Kernels for Machine Learning Applications in Precision Medicine

Privacy-preserving SVM on Outsourced Genomic Data via Secure Multi-party Computation