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Phlebotomine sand flies are of global significance as important vectors of human disease, transmitting bacterial, viral, and protozoan pathogens, including the devastating kinetoplastid parasites of the genusLeishmania, the causative agents of diseases collectively termed leishmaniasis. More than 40 pathogenicLeishmaniaspecies are transmitted to humans by approximately 35 sand fly species in 98 countries with hundreds of millions of people at risk around the world. As no approved efficacious vaccine exists, available drugs are expensive and/or toxic, and resistance is emerging, management of sand fly populations to break transmission is currently the most effective disease control strategy. To better understand the biology of sand flies, including the mechanisms involved in their vectorial capacity, insecticide resistance, and population structures we sequenced the genomes of two of the most important sand fly species:Phlebotomus papatasi, a cutaneous leishmaniasis vector, (distributed in the Middle East and North Africa) andLutzomyia longipalpis,a visceral leishmaniasis vector (distributed across Central and South America). We categorized and curated genes involved in processes important to their roles as disease vectors, including chemosensation, blood feeding, circadian rhythm, immunity, and detoxification, as well as mobile genetic elements. We also defined gene orthology and observed micro-synteny among the genomes. Finally, we present the genetic diversity and population structure of these species in their respective geographical areas. These genomes will be a foundation on which to base future efforts to prevent vector-borne transmission ofLeishmaniaparasites.Author SummaryThe leishmaniases are a group of neglected tropical diseases caused by protist parasites from the GenusLeishmania. DifferentLeishmaniaspecies present a wide clinical profile, ranging from mild, often self-resolving cutaneous lesions that can lead to protective immunity, to severe metastatic mucosal disease, to visceral disease that is ultimately fatal.Leishmaniaparasites are transmitted by the bites of sand flies, and as no approved vaccine exists, available drugs are toxic and/or expensive and resistance is emerging, new dual control strategies to combat these diseases must be developed, combining interventions on human infections and integrated sand fly population management. Effective vector control requires a good understanding of the biology of sand flies. To this end, we sequenced and annotated the genomes of two sand fly species that are important leishmaniasis vectors from the Old and New Worlds. These genomes allow us to better understand, at the genetic level, processes important in the vector biology of these species, such as finding hosts, blood-feeding, immunity, and detoxification. These genomic resources highlight the driving forces of evolution of two majorLeishmaniavectors and provide foundations for future research on how to better prevent leishmaniasis by control of the sand fly vectors.

Inversions occur when sections of a chromosome (DNA molecule) are completely reversed end-to-end. Large inversions (multiple megabases in length) can be detected, localized, and genotyped using principal component analysis (PCA) of single nucleotide polymorphisms (SNPs). However, detection and localization tasks are performed and interpreted manually. We propose a novel pipeline for the detection and localization tasks in an automated manner. We compare our results with manual analysis for localization and show that our algorithm can achieve a similarity score of 0.95 on average. For the classification task, we achieve an accuracy of 0.88 as compared to manual classification. Our results suggest that our proposed methods are fast and accurate for these tasks and can be used as tools for detection and localization.

Enhancers are short regions of non-coding DNA that increase transcription rates of genes despite being located distantly from the genes themselves [5]. Enhancers are identified through experimental techniques such as ChIP-Seq or CUT&RUN with H3K4me1 and H3K27ac histone modifications, self-transcribing active regulatory region sequencing (STARR-Seq), and massively parallel reporter assays (MPRA). Machine learning models have been used in conjunction with experimental data to identify enhancer activity from sequences [3], predict enhancer-transcription factor interactions [4], and decode the enhancer regulatory language [2]. We describe a framework that connects peak calling errors to the prediction accuracy of sequence models. The key assumptions of our framework are that (1) enhancers have consistent sequence patterns that can be used to separate enhancers from control sequences, (2) errors in the training data impact prediction accuracies in predictable ways, and (3) prediction accuracy is a useful proxy for evaluating peak calling accuracy. In the framework, data sets are constructed from peak (positive) and randomly sampled (control) sequences. Machine learning models are trained and evaluated on the sequences in a cross-chromosome (cross-fold) setup. Lastly, precision of the originating peaks are evaluated by calculating true and false positive rates. We applied our framework to evaluate peaks for D. melanogaster STARR-Seq data [1] called with the MACS software [6]. Although designed for ChIP-Seq data, MACS can be used to process other types of data, but users must be careful about parameter choices. We evaluated different parameter combinations with our framework and visual comparisons of called peaks. True and false positive rates ranged from a high of 88.0% to a low of 74.7% and from a low of 18.6% to a high of 49.4%, respectively. The default MACS parameters produced the highest true and lowest false positive rates, suggesting that the default parameters are also suitable for STARR-Seq data. Our results demonstrate the utility of our framework through a practical application and provide a base for future development.

When reference genome assemblies are updated, the peaks from DNA enrichment assays such as ChIP-Seq and FAIRE-Seq need to be called again using the new genome assembly. PeakMatcher is an open-source package that aids in validation by matching peaks across two genome assemblies using the alignment of reads or within the same genome. PeakMatcher calculates recall and precision while also outputting lists of peak-to-peak matches. PeakMatcher uses read alignments to match peaks across genome assemblies. PeakMatcher finds all read aligned to one genome that overlap with a given list of peaks. PeakMatcher uses the read names to locate where those reads are aligned against a second genome. Lastly, all peaks called against the second genome that overlap with the aligned reads are found and output. PeakMatcher groups uses the peak-read-peak relationships to discover 1-to-1, 1-to-many, and many-to-many relationships. Overlap queries are performed with interval trees for maximum efficiency. We evaluated PeakMatcher on two data sets. The first data set was FAIRE-Seq (Formaldehyde-Assisted Isolation of Regulatory Elements Sequencing) of DNA isolated embyros of the mosquito Aedes aegypti [2, 4]. We implemented a peak calling pipeline and validated it on the older (highly fragmented) AaegL3 assembly [5]. PeakMatcher matched 92.9% (precision) of the 121,594 previously-called peaks from [2, 4] with 89.4% (recall) of the 124,959 peaks called with our new pipeline. Next, we applied the peak-calling pipeline to call FAIRE peaks using the newer, chromosome-complete AaegL5 assembly [3]. PeakMatcher found matches for 14 of the 16 experimentally-validated AaegL3 FAIRE peaks from [2, 4]. We validated the matches by comparing nearby genes across the genomes. Nearby genes were consistent for 11 of the 14 peaks; inconsistencies for at least two of the remaining peaks were clearly attributable to differences in assemblies. When applied to all of the peaks, Peak-Matcher matched 78.8% (precision) of the 124,959 AaegL3 peaks with 76.7% (recall) of the 128,307 AaegL5 peaks. The second data set was STARR-Seq (Self-Transcribing Active Regulatory Region Sequencing) of Drosophila melanogaster DNA in S2 culture cells [1]. We called STARR peaks against two versions (dm3 and r5.53) of the D. melanogaster genome [6]. PeakMatcher matched 77.4% (precision) of the 4,195 dm3 peaks with 94.8% (recall) of the 3,114 r5.53 peaks. PeakMatcher and associated documentation are available on GitHub (https://github.com/rnowling/peak-matcher) under the open-source Apache Software License v2. PeakMatcher was written in Python 3 using the intervaltree library.

STARR-Seq is a high-throughput technique for directly identifying genomic regions with enhancer activity [1]. Genomic DNA is sheared, inserted into artificial plasmids designed so that DNA with enhancer activity trigger self-transcription, and transfected into culture cells. The resulting RNA is converted back into cDNA, sequenced, and aligned to a reference genome. "Peaks" are called by comparing observed read depth at each point to an expected read depth from control DNA using a statistical test. Examples of peak calling methods based on read depth include MACS2 [4], basicSTARRSeq, and STARRPeaker [3]. It is challenging to accurately distinguish between real peaks and artifacts in regions where mean read depth is low but the variance is high. Fortunately, enhancer activity is strongly correlated with sequence content. We propose using sequence-based machine learning models in a semi-supervised framework to filter peaks. 501-bp sequences centered on the a11k STARR peaks from [1] were extracted from the Drosophila melanogaster dm3 genome. Randomly-sampled 501-bp sequences were used as a negative set. Peaks were filtered using a Bonferroni-corrected significance value (α = 0.05) to create a "high-confidence" subset of a2.2k peaks. A Logistic Regression model with k-mer count features was trained on the high-confidence peak sequences and their negatives and used to classifying the remaining a8.8k peak sequences. The self-trained, sequenced-based model identified an additional a3.7k candidate enhancers ("medium confidence"). The remaining a5k STARR peaks were considered "low confidence" peaks. We plotted histograms of the read depth log-fold change for the three sets of peaks (high, medium, and low confidence) (see Figure 1). The distributions for the medium- and low-confidence peaks overlapped significantly. The sequence-based model identified enhancer candidates that would otherwise be filtered out using read depth alone. We called peaks for the 4 D. melanogaster FAIRE-Seq data sets from [2]. Sequencing data were cleaned with Trimmomatic, aligned to the dm3 genome with bwa backtrack, and filtered for mapping quality (q

Phlebotomine sand flies employ an elaborate system of pheromone communication wherein males produce pheromones that attract other males to leks (thus acting as an aggregation pheromone) and females to the lekking males (sex pheromone). In addition, the type of pheromone produced varies among populations. Despite the numerous studies on sand fly chemical communication, little is known of their chemosensory genome. Chemoreceptors interact with chemicals in an organism’s environment to elicit essential behaviors such as the identification of suitable mates and food sources. Thus, they play important roles during adaptation and speciation. Major chemoreceptor gene families, odorant receptors (ORs), gustatory receptors (GRs) and ionotropic receptors (IRs) together detect and discriminate the chemical landscape. Here, we annotated the chemoreceptor repertoire in the genomes of Lutzomyia longipalpis and Phlebotomus papatasi, major phlebotomine vectors in the New World and Old World, respectively. Comparison with other sequenced Diptera revealed a large and unique expansion where over 80% of the ~140 ORs belong to a single, taxonomically restricted clade. We next conducted a comprehensive analysis of the chemoreceptors in 63 L. longipalpis individuals from four different locations in Brazil representing allopatric and sympatric populations and three sex-aggregation pheromone types (chemotypes). Population structure based on single nucleotide polymorphisms (SNPs) and gene copy number in the chemoreceptors corresponded with their putative chemotypes, and corroborate previous studies that identified multiple populations. Our work provides genomic insights into the underlying behavioral evolution of sexual communication in the L. longipalpis species complex in Brazil, and highlights the importance of accounting for the ongoing speciation in central and South American Lutzomyia that could have important implications for vectorial capacity., Author summary Phlebotomine sand flies are the primary vectors of Leishmania parasites, the causative agents of cutaneous and visceral leishmaniasis. Due to the lack of vaccines, control of leishmaniasis relies upon reducing human exposure to sand flies. Sand flies produce sex-aggregation pheromones that elicit robust olfactory behaviors, and the molecular targets for pheromone detection remain unknown. We identified chemoreceptors in the genomes of L. longipalpis and P. papatasi, and used these gene models to explore chemoreceptor evolution in 63 L. longipalpis individuals representing different pheromone types. These analyses identified genomic loci underlying chemosensory behavior in sand flies. This paves the way for understanding the sand fly species diversity at the molecular level, and functional characterization of these candidate genes will isolate and identify chemostimuli that can directly be tested as potential attractants for odor-baited traps.

Background Large (>1 Mb), polymorphic inversions have substantial impacts on population structure and maintenance of genotypes. These large inversions can be detected from single nucleotide polymorphism (SNP) data using unsupervised learning techniques like PCA. Construction and analysis of a feature matrix from millions of SNPs requires large amount of memory and limits the sizes of data sets that can be analyzed. Methods We propose using feature hashing construct a feature matrix from a VCF file of SNPs for reducing memory usage. The matrix is constructed in a streaming fashion such that the entire VCF file is never loaded into memory at one time. Results When evaluated on Anopheles mosquito and Drosophila fly data sets, our approach reduced memory usage by 97% with minimal reductions in accuracy for inversion detection and localization tasks. Conclusion With these changes, inversions in larger data sets can be analyzed easily and efficiently on common laptop and desktop computers. Our method is publicly available through our open-source inversion analysis software, Asaph.

Chromosomal inversions are associated with reproductive isolation and adaptation in insects such as Drosophila melanogaster and the malaria vectors Anopheles gambiae and Anopheles coluzzii. While methods based on read alignment have been useful in humans for detecting inversions, these methods are less successful in insects due to long repeated sequences at the breakpoints. Alternatively, inversions can be detected using principal component analysis (PCA) of single nucleotide polymorphisms (SNPs). We apply PCA-based inversion detection to a simulated data set and real data from multiple insect species, which vary in complexity from a single inversion in samples drawn from a single population to analyzing multiple overlapping inversions occurring in closely-related species, samples of which that were generated from multiple geographic locations. We show empirically that proper analysis of these data can be challenging when multiple inversions or populations are present, and that our alternative framework is more robust in these more difficult scenarios.

Prostate segmentation is a necessary pre-processing step for computer-aided detection and diagnosis algorithms for prostate disorders and associated cancers. Deep learning models like U-Net offer the potential for performing classification and segmentation in a single step. We evaluated the U-Net model for prostate segmentation on 1,235 Magnetic Resonance (MR) images from 39 patients with prostate cancer. On our data set, the U-Net models generated a substantial number of false-positive predictions (average precision of 67.6%). We propose separating classification and segmentation into distinct tasks implemented via a pipeline. Our classifier achieved an average ROC AUC of 97.7% (SD 1.0%) in four-fold cross-fold validation. Compared to the U-Net model alone, our pipeline increased overall agreement between the predicted and human-annotated masks (Dice score of 0.907 vs 0.758 and precision of 83.2% vs 67.6%) without a significant loss in recall (83.4% vs 85.6%). Performing classification and segmentation in a single step is a desirable goal. Our results suggest, however, that improvements are still needed before U-Net offers comparable precision to using a separate classifier.

Association tests performed with the Likelihood-Ratio Test (LR Test) can be an alternative to [Formula: see text], which is often used in population genetics to find variants of interest. Because the LR Test has several properties that could make it preferable to [Formula: see text], we propose a novel approach for modeling unknown genotypes in highly-similar species. To show the effectiveness of this LR Test approach, we apply it to single-nucleotide polymorphisms (SNPs) associated with the recent speciation of the malaria vectors Anopheles gambiae and Anopheles coluzzii and compare to [Formula: see text].

Principal Component Analysis (PCA) of dense single nucleotide polymorphism (SNP) data has wide-ranging applications in populations genetics, including detection of chromosomal inversions. SNPs associated with each PC can be identified through single-SNP association tests performed between SNP genotypes and PC coordinates; this approach has several advantages over thresholding loading factors or sparse PCA methods. Insect vector SNP data often have a high proportion of unknown (uncalled) genotypes, however, that cannot be reliably imputed and prevent the direct usage of association tests. Building on our previous work, we propose a novel method for adjusting the association tests to handle these unknown genotypes. We demonstrate the utility of the method through two applications: detecting chromosomal inversions and characterizing differentiation processed captured by PCA. When applied to SNP data from the 2L and 2R chromosome arms of 34 karyotyped Anopheles gambiae and Anopheles coluzzii mosquitoes, our method clearly identifies the 2La, 2Rb, 2Rc, 2Rj, and 2Ru inversions. Using our method to identify SNP associated with 2L-PC3, we observed one of the two insecticide-resistance variants in the Rdl gene; our results suggests that the PC is capturing differentiation driven by insecticide usage.

Beyond automated classification, supervised machine-learning models can be interpreted to find which features or combination of features distinguish sets of classes. Logistic Regression (LR) is an example of a model well-suited for human interpretation. Unfortunately, results from feature ranking with LR may not be reliable and reproducible for the same dataset. We demonstrate that stability and consistency can be achieved via ensembles (“LR ensembles”). As a specific example of the real-world utility of our associated framework, we apply LR ensembles to single-nucleotide polymorphisms (SNPs) associated with the recent speciation of the malaria vectors Anopheles gambiae and Anopheles coluzzii and compare with the more common univariate metric F ST .

NMR spectroscopists are hindered by the lack of standardization for spectral data among the file formats for various NMR data processing tools. This lack of standardization is cumbersome as researchers must perform their own file conversion in order to switch between processing tools and also restricts the combination of tools employed if no conversion option is available. The CONNJUR Spectrum Translator introduces a new, extensible architecture for spectrum translation and introduces two key algorithmic improvements. This first is translation of NMR spectral data (time and frequency domain) to a single in-memory data model to allow addition of new file formats with two converter modules, a reader and a writer, instead of writing a separate converter to each existing format. Secondly, the use of layout descriptors allows a single fid data translation engine to be used for all formats. For the end user, sophisticated metadata readers allow conversion of the majority of files with minimum user configuration. The open source code is freely available at http://connjur.sourceforge.net for inspection and extension. Electronic supplementary material The online version of this article (doi:10.1007/s10858-011-9497-1) contains supplementary material, which is available to authorized users.

Generating large amounts of semantically-rich data for testing big data workflows is paramount for scalable performance benchmarking and quality assurance in modern machine-learning and analytics workloads. The most obvious use case for such a generative algorithm is in conjunction with a big data application blueprint, which can be used by developers (to test their emerging big data solutions) as well as end users (as a starting point for validating infrastructure installations, building novel applications, and learning analytics methods). We present a new domain-driven, generative data model for Big Pet Store, a big data application blueprint for the Hadoop ecosystem included in the Apache Big Top distribution. We describe the model and demonstrate its ability to generate semantically-rich data at variable scale ranging from a single machine to a large cluster. We validate the model by using the generated data to answer questions about customer locations and purchasing habits for a fictional targeted advertising campaign, a common business use case.

The problem of formatting data so that it conforms to the required input for scientific data processing tools pervades scientific computing. The CONNecticut Joint University Research Group (CONNJUR) has developed a data translation tool based on a pipeline architecture that partially solves this problem. The CONNJUR Spectrum Translator supports data format translation for experiments that use Nuclear Magnetic Resonance to determine the structure of large protein molecules.

Encoding for solutions of combinatorial optimization problems involving permutations or constraints that maintain the generality of operators in evolutionary computation is often difficult. In this paper, we present the Priority Encoding Scheme (PES), a general-purpose encoding scheme that encodes information used to construct solutions rather than directly encoding solutions themselves. We show that not only is PES simple to implement, but that it can be used effectively with Genetic Algorithms (GA) and Simulated Annealing (SA) to find good solutions to the multiple-constraint knapsack problem (MKP) and shows promise for finding good solutions to the traveling salesman problem (TSP).

The CONNecticut Joint University Research (CONNJUR) team is a group of biochemical and software engineering researchers at multiple institutions. The vision of the team is to develop a comprehensive application that integrates a variety of existing analysis tools with workflow and data management to support the process of protein structure determination using Nuclear Magnetic Resonance (NMR). The use of multiple disparate tools and lack of data management, currently the norm in NMR data processing, provides strong motivation for such an integrated environment. This manuscript briefly describes the domain of NMR as used for protein structure determination and explains the formation of the CONNJUR team and its operation in developing the CONNJUR application. The manuscript also describes the evolution of the CONNJUR application through four prototypes and describes the challenges faced while developing the CONNJUR application and how those challenges were met.

Background One of the most important developments in bioinformatics over the past few decades has been the observation that short linear peptide sequences (minimotifs) mediate many classes of cellular functions such as protein-protein interactions, molecular trafficking and post-translational modifications. As both the creators and curators of a database which catalogues minimotifs, Minimotif Miner, the authors have a unique perspective on the commonalities of the many functional roles of minimotifs. There is an obvious usefulness in standardizing functional annotations both in allowing for the facile exchange of data between various bioinformatics resources, as well as the internal clustering of sets of related data elements. With these two purposes in mind, the authors provide a proposed syntax for minimotif semantics primarily useful for functional annotation. Results Herein, we present a structured syntax of minimotifs and their functional annotation. A syntax-based model of minimotif function with established minimotif sequence definitions was implemented using a relational database management system (RDBMS). To assess the usefulness of our standardized semantics, a series of database queries and stored procedures were used to classify SH3 domain binding minimotifs into 10 groups spanning 700 unique binding sequences. Conclusion Our derived minimotif syntax is currently being used to normalize minimotif covalent chemistry and functional definitions within the MnM database. Analysis of SH3 binding minimotif data spanning many different studies within our database reveals unique attributes and frequencies which can be used to classify different types of binding minimotifs. Implementation of the syntax in the relational database enables the application of many different analysis protocols of minimotif data and is an important tool that will help to better understand specificity of minimotif-driven molecular interactions with proteins.