Workflow Steps Research Articles

Design and optimization of a 16S microbial qPCR multiplex for the presumptive identification of feces, saliva, vaginal and menstrual secretions.

Molecular methods for body fluid identification have been extensively researched in the forensic community over the last decade, mostly focusing on RNA‐based methods. Microbial DNA analysis has long been used for forensic applications, such as postmortem interval estimations, but only recently has it been applied to body fluid identification. High‐throughput sequencing of the 16S ribosomal RNA gene by previous research groups revealed that microbial signatures and abundances vary across human body fluids at the genus and/or species taxonomic level. Since quantitative PCR is still the current technique used in forensic DNA analysis, the purpose of this study was to design a qPCR multiplex targeting the 16S gene of Bacteroides uniformis, Streptococcus salivarius, and Lactobacillus crispatus that can distinguish between feces, saliva, and vaginal/menstrual secretions, respectively. Primers and probes were designed at the species level because these bacteria are highly abundant within their respective fluid. The validated 16S triplex was evaluated in DNA extracts from thirty donors of each body fluid. A classification regression tree model resulted in 96.5% classification accuracy of the population data, which demonstrates the ability of this 16S triplex to presumptively identify these fluids with high confidence at the quantification step of the forensic workflow using minimal input volume of DNA extracted from evidentiary samples.

Using DNA Metabarcoding to Identify Floral Visitation by Pollinators

The identification of floral visitation by pollinators provides an opportunity to improve our understanding of the fine-scale ecological interactions between plants and pollinators, contributing to biodiversity conservation and promoting ecosystem health. In this review, we outline the various methods which can be used to identify floral visitation, including plant-focused and insect-focused methods. We reviewed the literature covering the ways in which DNA metabarcoding has been used to answer ecological questions relating to plant use by pollinators and discuss the findings of this research. We present detailed methodological considerations for each step of the metabarcoding workflow, from sampling through to amplification, and finally bioinformatic analysis. Detailed guidance is provided to researchers for utilisation of these techniques, emphasising the importance of standardisation of methods and improving the reliability of results. Future opportunities and directions of using molecular methods to analyse plant–pollinator interactions are then discussed.

Model and system for scientific workflows represented in file system directory tree

In this paper, we present a new workflow representation model which is based on directory trees. We show the implementation of the model inside the EPISODES Platform — a portal for analyses related to anthropogenic hazards. Most workflow management systems use a DAG or custom programming language to encode a workflow. While this works well in many cases, we believe that there is a class of applications for which our approach is a better fit.This paper demonstrates how workflows can be efficiently represented in a file system tree. In our model, each workflow step is encoded as a directory. This enables natural encapsulation and modularity of the data alongside the workflow structure. We show how fundamental patterns such as sequence, merge, fork, and cycle are encoded. We believe that the presented directory tree-based representation is well suited for workflow systems that emphasize interactive composition processes. By implementing a rich GUI with built-in data visualization tools it may be particularly useful for exploratory programming where interactivity, dynamicity, and extensibility are crucial. The solution supports multi-level workflow nesting, reuse, sharing, and live collaboration in the workflow design process. The integrated workflow execution engine is scalable and supports automatic execution on high-performance distributed infrastructure. Our model was implemented and evaluated within the EPISODES Platform (Orlecka-Sikora et al., 2020), a workflow management system used by over 1400 users interested in research into anthropogenic hazards.

Insights Gained From a Re-analysis of Five Improvement Cases in Healthcare Integrating System Dynamics Into Action Research.

Healthcare is complex with multi-professional staff and a variety of patient care pathways. Time pressure and minimal margins for errors, as well as tension between hierarchical power and the power of the professions, make it challenging to implement new policies or procedures. This paper explores five improvement cases in healthcare integrating system dynamics (SD) into action research (AR), aiming to identify methodological aspects of how this integration supported multi-professional groups to discover workable solutions to work-related challenges. This re-analysis was conducted by a multi-disciplinary research group using an iterative abductive approach applying qualitative analysis to structure and understand the empirical material. Frameworks for consultancy assignments/client projects were used to identify case project stages (workflow steps) and socio-analytical questions were used to bridge between the AR and SD perspectives. All studied cases began with an extensive AR-inspired inventory of problems/objectives and ended with an SD-facilitated experimental phase where mutually agreed solutions were tested in silico. Time was primarily divided between facilitated group discussions during meetings and modelling work between meetings. Work principles ensured that the voice of each participant was heard, inspired engagement, interaction, and exploratory mutual learning activities. There was an overall pattern of two major divergent and convergent phases, as each group moved towards a mutually developed point of reference for their problem/objective and solution, a case-specific multi-professional knowledge repository. By integrating SD into AR, more favourable outcomes for the client organization may be achieved than when applying either approach in isolation. We found that SD provided a platform that facilitated experiential learning in the AR process. The identified results were calibrated to local needs and circumstances, and compared to traditional top-down implementation for change processes, improved the likelihood of sustained actualisation.

Total workflow uncertainty of frameless radiosurgery with the Gamma Knife Icon cone-beam computed tomography.

ObjectiveFrameless treatment with the Gamma Knife Icon is still relatively new as a treatment option. As a result, additional confidence/knowledge about the uncertainty that exists within each portion of the treatment workflow could be gained especially regarding steps that have not been previously studied in the literature.MethodsThe Icon base delivery device (Perfexion) uncertainty is quantified and validated. The novel portions of the Icon such as mask immobilization, cone‐beam computed tomography image guidance, and the intrafraction motion management methods are studied specifically and to a greater extent to determine a total workflow uncertainty of frameless treatment with the Icon.ResultsThe uncertainty of each treatment workflow step has been identified with the total workflow uncertainty being identified in this work as 1.3 mm with a standard deviation of 0.51 mm.ConclusionThe total uncertainty of frameless treatment with the Icon has been evaluated and this data may indicate the need for setup margin in this setting with data that could be used by other institutions to calculate needed setup margin per their preferred recipe after validation of this data in their context.

Implementation and evaluation of social determinants of health practice models within community pharmacy

BackgroundWhile community pharmacies are an ideal setting for social needs screening and referral programs, information on social risk assessment within pharmacy practice is limited. ObjectivesOur primary objective was to describe 2 social determinant of health (SDOH) practice models implemented within community pharmacies. The secondary objective was to evaluate implementation practices utilizing the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework. Practice descriptionTwo pharmacy groups participated in a 3-month study, one in New York (9 pharmacies) and another in Missouri (1 pharmacy). The New York pharmacies implemented an SDOH specialist practice model, in which pharmacy staff members facilitate the program. The Missouri pharmacy implemented a community health worker (CHW) model by cross training their technicians. Each pharmacy developed their program using the Community Pharmacy Enhanced Services Network Care Model. Practice innovationBoth programs expanded the technician role to take on additional responsibilities. The SDOH specialist model partnered with a local independent practice association to create a social needs referral program using a technology platform for closed-loop communication. All workflow steps of the self-contained CHW program were completed within the pharmacy, placing additional responsibility on the CHW and pharmacy staff. Evaluation methodsRE-AIM framework dimensions of Reach, Effectiveness, and Adoption. ResultsSocial challenges were identified in 49 of 76 (65%) generated SDOH screenings. The most prevalent social needs reported were affordability of daily needs (33%) and health care system navigation (15%). While most pharmacy staff indicated that workflow steps were clearly defined, assessments and referral tools were identified as potential gaps. While approximately 50% of pharmacy staff were comfortable with their assigned roles and in addressing SDOH challenges, physical and mental health concerns required additional education for intervention. ConclusionThe successful implementation of community pharmacy SDOH programs connected patients with local resources. Community pharmacies are ideally positioned to expand their public health footprint through SDOH interactions that consequently improve patient care.

Prospective Evaluation of Prostate and Organs at Risk Segmentation Software for MRI-based Prostate Radiation Therapy.

The segmentation of the prostate and surrounding organs at risk (OARs) is a necessary workflow step for performing dose-volume histogram analyses of prostate radiation therapy procedures. Low-dose-rate prostate brachytherapy (LDRPBT) is a curative prostate radiation therapy treatment that delivers a single fraction of radiation over a period of days. Prior studies have demonstrated the feasibility of fully convolutional networks to segment the prostate and surrounding OARs for LDRPBT dose-volume histogram analyses. However, performance evaluations have been limited to measures of global similarity between algorithm predictions and a reference. To date, the clinical use of automatic segmentation algorithms for LDRPBT has not been evaluated, to the authors' knowledge. The purpose of this work was to assess the performance of fully convolutional networks for prostate and OAR delineation on a prospectively identified cohort of patients who underwent LDRPBT by using clinically relevant metrics. Thirty patients underwent LDRPBT and were imaged with fully balanced steady-state free precession MRI after implantation. Custom automatic segmentation software was used to segment the prostate and four OARs. Dose-volume histogram analyses were performed by using both the original automatically generated contours and the physician-refined contours. Dosimetry parameters of the prostate, external urinary sphincter, and rectum were compared without and with the physician refinements. This study observed that physician refinements to the automatic contours did not significantly affect dosimetry parameters. Keywords: MRI, Neural Networks, Radiation Therapy, Radiation Therapy/Oncology, Genital/Reproductive, Prostate, Segmentation, Dosimetry Supplemental material is available for this article. © RSNA, 2022.

FILER: a framework for harmonizing and querying large-scale functional genomics knowledge.

ABSTRACTQuerying massive functional genomic and annotation data collections, linking and summarizing the query results across data sources/data types are important steps in high-throughput genomic and genetic analytical workflows. However, these steps are made difficult by the heterogeneity and breadth of data sources, experimental assays, biological conditions/tissues/cell types and file formats. FILER (FunctIonaL gEnomics Repository) is a framework for querying large-scale genomics knowledge with a large, curated integrated catalog of harmonized functional genomic and annotation data coupled with a scalable genomic search and querying interface. FILER uniquely provides: (i) streamlined access to >50 000 harmonized, annotated genomic datasets across >20 integrated data sources, >1100 tissues/cell types and >20 experimental assays; (ii) a scalable genomic querying interface; and (iii) ability to analyze and annotate user’s experimental data. This rich resource spans >17 billion GRCh37/hg19 and GRCh38/hg38 genomic records. Our benchmark querying 7 × 109 hg19 FILER records shows FILER is highly scalable, with a sub-linear 32-fold increase in querying time when increasing the number of queries 1000-fold from 1000 to 1 000 000 intervals. Together, these features facilitate reproducible research and streamline integrating/querying large-scale genomic data within analyses/workflows. FILER can be deployed on cloud or local servers (https://bitbucket.org/wanglab-upenn/FILER) for integration with custom pipelines and is freely available (https://lisanwanglab.org/FILER).

Time-lapse seismic data inversion for estimating reservoir parameters using deep learning

Geologic carbon sequestration involves the injection of captured carbon dioxide ([Formula: see text]) into subsurface formations for long-term storage. The movement and fate of the injected [Formula: see text] plume is of great concern to regulators because monitoring helps to identify potential leakage zones and determines the possibility of safe long-term storage. To address this concern, we design a deep-learning framework for [Formula: see text] saturation monitoring to determine the geologic controls on the storage of the injected [Formula: see text]. We use different combinations of porosities and permeabilities for a given reservoir to generate saturation and velocity models. We train the deep-learning model with a few time-lapse seismic images and their corresponding changes in saturation values for a particular [Formula: see text] injection site. The deep-learning model learns the mapping from the change in the time-lapse seismic response to the change in [Formula: see text] saturation during the training phase. We then apply the trained model to data sets comprising different time-lapse seismic image slices (corresponding to different time instances) generated using different porosity and permeability distributions that are not part of the training to estimate the [Formula: see text] saturation values along with the plume extent. Our algorithm provides a deep-learning assisted framework for the direct estimation of [Formula: see text] saturation values and plume migration in heterogeneous formations using the time-lapse seismic data. Our method improves the efficiency of time-lapse inversion by streamlining the large number of intermediate steps in the conventional time-lapse inversion workflow. This method also helps to incorporate the geologic uncertainty for a given reservoir by accounting for the statistical distribution of porosity and permeability during the training phase. Tests on different examples verify the effectiveness of our approach.

A review of Earth Artificial Intelligence

In recent years, Earth system sciences are urgently calling for innovation on improving accuracy, enhancing model intelligence level, scaling up operation, and reducing costs in many subdomains amid the exponentially accumulated datasets and the promising artificial intelligence (AI) revolution in computer science. This paper presents work led by the NASA Earth Science Data Systems Working Groups and ESIP machine learning cluster to give a comprehensive overview of AI in Earth sciences. It holistically introduces the current status, technology, use cases, challenges, and opportunities, and provides all the levels of AI practitioners in geosciences with an overall big picture and to “blow away the fog to get a clearer vision” about the future development of Earth AI. The paper covers all the majorspheres in the Earth system and investigates representative AI research in each domain. Widely used AI algorithms and computing cyberinfrastructure are briefly introduced. The mandatory steps in a typical workflow of specializing AI to solve Earth scientific problems are decomposed and analyzed. Eventually, it concludes with the grand challenges and reveals the opportunities to give some guidance and pre-warnings on allocating resources wisely to achieve the ambitious Earth AI goals in the future.

Toward a Practical Appraisal for Waveform Tomography of Band- and Offset-Limited Marine Seismic Data

We present a generalized workflow to retrieve high-resolution P-wave velocity ( <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex>$V_{ P}$</tex> </formula> ) models of complex Earth's subsurface structures from traditional marine near-vertical seismic reflection experiments. These records have typically offsets too short to map refraction phases and lack low-frequency information. The workflow is composed of three steps: 1) downward continuation (DC) of seismic records to the seafloor to recover diving wave information; 2) travel-time tomography (TTT) of first arrivals obtained from DC data, to retrieve a kinematically correct model; and 3) full-waveform inversion (FWI) of the original streamer dataset, starting with the model obtained with TTT and sequentially introducing higher wavenumber details into the model. We show that the TTT allows overcoming the issues associated with the nonlinearity intrinsic to FWI. We also disentangle envelope and phase from the waveform to choose the objective function most suitable for FWI. We assess the accuracy of initial models and predict the quality of the FWI results by quantifying the early arrival cycle skipping between original and simulated data. The efficiency of the workflow is tested with a challenging synthetic target model, containing vertical boundaries with strong velocity contrasts and velocity inversions embedded in a checkerboard-like pattern. We show that workflow steps 1) and 2) provide a TTT model that is not cycle skipped at the frequencies available in most marine seismic experiments and thus allow step 3) FWI to obtain high-resolution <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex>$V_{ P}$</tex> </formula> models of the subsurface using band- and offset-limited field datasets, traditionally collected in marine airgun and streamer acquisitions.

Progress in a Time Loop

Progress in a Time Loop

Statistics-Guided Residual Dictionary Learning for Footprint Noise Removal

The footprint noise is a type of coherent noise that arises from the acquisition geometry. The footprint noise is commonly seen in 3-D seismic volumes and greatly affects the amplitude-based processing and interpretation steps in the seismic exploration workflow. Thus, removal of the footprint noise is necessary for warranting a reliable interpretation output of seismic data processing. However, because footprint noise is usually weak and also spatially coherent, it is inevitable to cause damages to useful signals during its removal steps. Here, we propose a dictionary learning (DL)-based method to effectively remove the footprint noise. We design an algorithm framework to effectively learn the dictionary atoms of the signal waveforms and separate the features of the footprint noise from the learned atoms. Considering the special features of the footprint noise in the dictionary atoms, we propose a statistics-guided way to separate the dictionary atoms into footprint-affected and footprint-free atoms. Then, the footprint-affected atoms are processed via a 2-D median filtering step. The combination between the untouched footprint-free atoms and filtered footprint-affected atoms result in a better dictionary of the signal waveforms and the footprint atoms. We use residual DL to encode the input data by a linear combination of signal atoms and footprint atoms. Removal of footprint atoms and their corresponding sparse coefficients leads to a successful footprint removal. We use both 3-D synthetic and field data examples to demonstrate the effectiveness of the proposed method.

Design and Implementation of a Guideline-Based Workflow Software System for Improving the Chemotherapy Process

Background: While chemotherapy is an effective method for the treatment of patients with cancer, it is a complex, multidisciplinary, and error-prone process. Paper-based protocols are commonly applied in chemotherapy; however, they fail to eliminate the complexity of this process. Therefore, a new guideline-based workflow software (GWS) system is needed to improve the workflow and quality of chemotherapy process. Methods: Planning was initiated 11 months before the system implementation and it involved a multidisciplinary group to analyze the current chemotherapy workflow and protocols for identifying the workflow components, analyzing paper-based protocols, developing computer-based protocols, and designing of systems based on an object-oriented analysis. To implement the GWS, we applied a system based on Python programming language and SQL language. Results: The conceptual model was developed based on need assessments and chemotherapy steps. A minimum dataset was developed for the electronic health records. We established examination forms for the patient management system (PMS), as well as specific standard forms for chemotherapy ordering, prescription verification and administration templates. Finally, developed GWS system consisted of a PMS, computerized provider order entry (CPOE), prescription verification system (PVS), and nursing administration system (NAS). Conclusions: A PMS, a PVS, a NAS, and a guideline-based clinical decision support system were integrated into the CPOE system to improve the chemotherapy process. Elimination of iterations and unnecessary steps in old chemotherapy workflow, improving patient safety, improvement of communication and coordination between healthcare providers, and use of updated evidence-based medicine in direct chemotherapy orders justify the deployment of GWS in the cancer care settings.

Phylogenomic and mitogenomic data can accelerate inventorying of tropical beetles during the current biodiversity crisis.

Conservation efforts must be evidence-based, so rapid and economically feasible methods should be used to quantify diversity and distribution patterns. We have attempted to overcome current impediments to the gathering of biodiversity data by using integrative phylogenomic and three mtDNA fragment analyses. As a model, we sequenced the Metriorrhynchini beetle fauna, sampled from ~700 localities in three continents. The species-rich dataset included ~6500 terminals, ~ 1850 putative species delimited at 5% uncorrected pairwise threshold, possibly ~1000 of them unknown to science. Neither type of data could alone answer our questions on biodiversity and phylogeny. The phylogenomic backbone enabled the integrative delimitation of robustly defined natural genus-group units that will inform future research. Using constrained mtDNA analysis, we identified the spatial structure of species diversity, very high species-level endemism, and a biodiversity hotspot in New Guinea. We suggest that focused field research and subsequent laboratory and bioinformatic workflow steps would substantially accelerate the inventorying of any hyperdiverse tropical group with several thousand species. The outcome would be a scaffold for the incorporation of further data from environmental sequencing and ecological studies. The database of sequences could set a benchmark for the spatiotemporal evaluation of biodiversity, would support evidence-based conservation planning, and would provide a robust framework for systematic, biogeographic, and evolutionary studies.

PlantR: An R package and workflow for managing species records from biological collections

Abstract Species records from biological collections are becoming increasingly available online. This unprecedented availability of records has largely supported recent studies in taxonomy, biogeography, macroecology and biodiversity conservation. Biological collections vary in their documentation and notation standards, which have changed through time. For different reasons, neither collections nor data repositories perform the editing, formatting and standardisation of the data, leaving these tasks to the final users of the species records (e.g. taxonomists, ecologists and conservationists). These tasks are challenging, particularly when working with millions of records from hundreds of biological collections. To help collection curators and final users perform those tasks, we introduce plantR, an open‐source package that provides a comprehensive toolbox to manage species records from biological collections. The package is accompanied by the proposal of a reproducible workflow to manage this type of data in taxonomy, ecology and biodiversity conservation. It is implemented in R and designed to handle relatively large datasets as fast as possible. Initially designed to handle plant species records, many of the plantR features also apply to other groups of organisms, given that the data structure is similar. The plantR workflow includes tools to (a) download records from different data repositories, (b) standardise typical fields associated with species records, (c) validate the locality, geographical coordinates, taxonomic nomenclature and species identifications, including the retrieval of duplicates across collections, and (d) summarise and export records, including the construction of species lists with vouchers. Other R packages provide tools to tackle some of the workflow steps described above. But in addition to the new tools and resources related to data standardisation and validation, the greatest strength of plantR is to provide a comprehensive and user‐friendly workflow in one single environment, performing all tasks from data retrieval to export. Thus, plantR can help researchers better assess data quality and avoid data leakage in a wide variety of studies using species records.

Big Data Workflows: Locality-Aware Orchestration Using Software Containers

The emergence of the edge computing paradigm has shifted data processing from centralised infrastructures to heterogeneous and geographically distributed infrastructures. Therefore, data processing solutions must consider data locality to reduce the performance penalties from data transfers among remote data centres. Existing big data processing solutions provide limited support for handling data locality and are inefficient in processing small and frequent events specific to the edge environments. This article proposes a novel architecture and a proof-of-concept implementation for software container-centric big data workflow orchestration that puts data locality at the forefront. The proposed solution considers the available data locality information, leverages long-lived containers to execute workflow steps, and handles the interaction with different data sources through containers. We compare the proposed solution with Argo workflows and demonstrate a significant performance improvement in the execution speed for processing the same data units. Finally, we carry out experiments with the proposed solution under different configurations and analyze individual aspects affecting the performance of the overall solution.

Online pathology request platform at the Semmelweis University

The 1st Department of Pathology and Experimental Cancer Research, Semmelweis University (Budapest, Hungary) has a broad network of clinical partners, many of which are non-university hospitals. A separate hospital information system and a local laboratory workflow management system is used at the Department. University clinics use the hospital information system for electronic requesting of tests. Non-university partners have no access to the systems, requesting tests is paper-based, registration of the requests at the pathology lab is manual and laborious. Our main objective was to improve the efficiency of the sample registration step of the pathology workflow. Applying the Plan-Do-Check-Act procedure, a quality improvement project has been carried out and an online, subspecialty-based requesting application tool, interfaced with the current laboratory information system, was developed. The average sample registration time improved with 65% among the early user partners. The past years have shown smaller, inefficient pathology labs decreasing in number and integrated into larger regional diagnostic centers. Both issues of efficiency and quality assurance and problems rooted in human resources are drivers of further centralisation. The numbers of test requests and samples from non in-house partners are expected to be increased in the pathology labs in the future. Efficient and safe sample tracking has to start at the site of sample acquisition. State of the art laboratory information systems should support this expansion of competence. Orv Hetil. 2021; 162(49): 1962-1967.

Time to get real with qPCR controls: The frequency of sample contamination and the informative power of negative controls in environmental DNA studies.

Environmental (e)DNA methods have enabled rapid, sensitive and specific inferences of taxa presence throughout diverse fields of ecological study. However, use of eDNA results for decision-making has been impeded by uncertainties associated with false positive tests putatively caused by sporadic or systemic contamination. Sporadic contamination is a process that is inconsistent across samples and systemic contamination occurs consistently over a group of samples. Here, we used empirical data and laboratory experiments to (i) estimate the sporadic contamination rate for each stage of a common, targeted eDNA workflow employing best practice quality control measures under simulated conditions of rare and common target DNA presence, (ii) determine the rate at which negative controls (i.e., "blanks") detect varying concentrations of systemic contamination, and (iii) estimate the effort that would be required to consistently detect sporadic and systemic contamination. Sporadic contamination rates were very low across all eDNA workflow steps, and, therefore, an intractably high number of negative controls (>100) would be required to determine occurrence of sporadic contamination with any certainty. Contrarily, detection of intentionally introduced systemic contamination was more consistent; therefore, very few negative controls (<5) would be needed to consistently alert to systemic contamination. These results have considerable implications to eDNA study design when resources for sample analyses are constrained.

A Practical Guide to Small Protein Discovery and Characterization Using Mass Spectrometry.

ABSTRACTSmall proteins of up to ∼50 amino acids are an abundant class of biomolecules across all domains of life. Yet due to the challenges inherent in their size, they are often missed in genome annotations, and are difficult to identify and characterize using standard experimental approaches. Consequently, we still know few small proteins even in well-studied prokaryotic model organisms. Mass spectrometry (MS) has great potential for the discovery, validation, and functional characterization of small proteins. However, standard MS approaches are poorly suited to the identification of both known and novel small proteins due to limitations at each step of a typical proteomics workflow, i.e., sample preparation, protease digestion, liquid chromatography, MS data acquisition, and data analysis. Here, we outline the major MS-based workflows and bioinformatic pipelines used for small protein discovery and validation. Special emphasis is placed on highlighting the adjustments required to improve detection and data quality for small proteins. We discuss both the unbiased detection of small proteins and the targeted analysis of small proteins of interest. Finally, we provide guidelines to prioritize novel small proteins, and an outlook on methods with particular potential to further improve comprehensive discovery and characterization of small proteins.