Resources for image-based high-throughput phenotyping in crops and data sharing challenges

High-throughput phenotyping (HTP) platforms are capable of monitoring the phenotypic variation of plants through multiple types of sensors, such as red green and blue (RGB) cameras, hyperspectral sensors, and computed tomography, which can be associated with environmental and genotypic data. Because of the wide range of information provided, HTP datasets represent a valuable asset to characterize crop phenotypes. As HTP becomes widely employed with more tools and data being released, it is important that researchers are aware of these resources and how they can be applied to accelerate crop improvement. Researchers may exploit these datasets either for phenotype comparison or employ them as a benchmark to assess tool performance and to support the development of tools that are better at generalizing between different crops and environments. In this review, we describe the use of image-based HTP for yield prediction, root phenotyping, development of climate-resilient crops, detecting pathogen and pest infestation, and quantitative trait measurement. We emphasize the need for researchers to share phenotypic data, and offer a comprehensive list of available datasets to assist crop breeders and tool developers to leverage these resources in order to accelerate crop breeding.

Various approaches are used to analyze high-throughput phenotyping data and tools can be developed and assessed using available image-based datasets.     

Genomics Virtual Laboratory: A Practical Bioinformatics Workbench for the Cloud

Background

Analyzing high throughput genomics data is a complex and compute intensive task, generally requiring numerous software tools and large reference data sets, tied together in successive stages of data transformation and visualisation. A computational platform enabling best practice genomics analysis ideally meets a number of requirements, including: a wide range of analysis and visualisation tools, closely linked to large user and reference data sets; workflow platform(s) enabling accessible, reproducible, portable analyses, through a flexible set of interfaces; highly available, scalable computational resources; and flexibility and versatility in the use of these resources to meet demands and expertise of a variety of users. Access to an appropriate computational platform can be a significant barrier to researchers, as establishing such a platform requires a large upfront investment in hardware, experience, and expertise.

Results

We designed and implemented the Genomics Virtual Laboratory (GVL) as a middleware layer of machine images, cloud management tools, and online services that enable researchers to build arbitrarily sized compute clusters on demand, pre-populated with fully configured bioinformatics tools, reference datasets and workflow and visualisation options. The platform is flexible in that users can conduct analyses through web-based (Galaxy, RStudio, IPython Notebook) or command-line interfaces, and add/remove compute nodes and data resources as required. Best-practice tutorials and protocols provide a path from introductory training to practice. The GVL is available on the OpenStack-based Australian Research Cloud (http://nectar.org.au) and the Amazon Web Services cloud. The principles, implementation and build process are designed to be cloud-agnostic.

Conclusions

This paper provides a blueprint for the design and implementation of a cloud-based Genomics Virtual Laboratory. We discuss scope, design considerations and technical and logistical constraints, and explore the value added to the research community through the suite of services and resources provided by our implementation.     

PPICurator: A Tool for Extracting Comprehensive Protein–Protein Interaction Information

Protein–protein interaction extraction through biological literature curation is widely employed for proteome analysis. There is a strong need for a tool that can assist researchers in extracting comprehensive PPI information through literature curation, which is critical in research on protein, for example, construction of protein interaction network, identification of protein signaling pathway, and discovery of meaningful protein interaction. However, most of current tools can only extract PPI relations. None of them are capable of extracting other important PPI information, such as interaction directions, effects, and functional annotations. To address these issues, this paper proposes PPICurator, a novel tool for extracting comprehensive PPI information with a variety of logic and syntax features based on a new support vector machine classifier. PPICurator provides a friendly web‐based user interface. It is a platform that automates the extraction of comprehensive PPI information through literature, including PPI relations, as well as their confidential scores, interaction directions, effects, and functional annotations. Thus, PPICurator is more comprehensive than state‐of‐the‐art tools. Moreover, it outperforms state‐of‐the‐art tools in the accuracy of PPI relation extraction measured by F‐score and recall on the widely used open datasets. PPICurator is available at https://ppicurator.hupo.org.cn .     

A Toolkit for ARB to Integrate Custom Databases and Externally Built Phylogenies

Researchers are perpetually amassing biological sequence data. The computational approaches employed by ecologists for organizing this data (e.g. alignment, phylogeny, etc.) typically scale nonlinearly in execution time with the size of the dataset. This often serves as a bottleneck for processing experimental data since many molecular studies are characterized by massive datasets. To keep up with experimental data demands, ecologists are forced to choose between continually upgrading expensive in-house computer hardware or outsourcing the most demanding computations to the cloud. Outsourcing is attractive since it is the least expensive option, but does not necessarily allow direct user interaction with the data for exploratory analysis. Desktop analytical tools such as ARB are indispensable for this purpose, but they do not necessarily offer a convenient solution for the coordination and integration of datasets between local and outsourced destinations. Therefore, researchers are currently left with an undesirable tradeoff between computational throughput and analytical capability. To mitigate this tradeoff we introduce a software package to leverage the utility of the interactive exploratory tools offered by ARB with the computational throughput of cloud-based resources. Our pipeline serves as middleware between the desktop and the cloud allowing researchers to form local custom databases containing sequences and metadata from multiple resources and a method for linking data outsourced for computation back to the local database. A tutorial implementation of the toolkit is provided in the supporting information, S1 Tutorial. Availability: http://www.ece.drexel.edu/gailr/EESI/tutorial.php.     

Clinical Applicability of Patient- and Clinician-Reported Outcome Tools in the Management of Patients With Acromegaly

ObjectiveWe aimed to assess treatment outcomes and disease control status in patients with acromegaly using patient- and clinician-reported outcome tools and to analyze correlations among different components of both tools.MethodsThis cross-sectional study included 72 patients from a national referral center with a median follow-up of 8 (5-12) years. The baseline SAGIT score at diagnosis was determined retrospectively, whereas the follow-up SAGIT and acromegaly quality of life questionnaire (AcroQoL) results were assessed at the most recent visit and by additional telephone interviews.ResultsAll SAGIT subscores decreased significantly from baseline to follow-up (global score from 14 to 4 [P < .001]). The SAGIT scores at baseline did not discriminate the current disease control status. However, a higher baseline SAGIT score and subscore T were associated with uncontrolled disease after the first-line treatment. Diagnostic delay was correlated with baseline S, A, G, and global SAGIT scores. At the follow-up, the global SAGIT score discriminated between cured/controlled and uncontrolled groups (4 vs 6 [P = .007]). The AcroQoL score was 69.3, with the personal relations subscale being the least affected and the physical scale being the most affected. There was no difference in the AcroQoL score between patients classified as uncontrolled or cured/controlled. At baseline and follow-up, there were significant negative correlations between S and A subscores and AcroQoL score. A higher body mass index, the presence of swelling, joint symptoms, headaches, sleep apnea, and hypertension significantly impaired quality of life.ConclusionOur results emphasize the complementary nature of the patient- and clinician-reported outcome tools in acromegaly management. We identified modifiable signs, symptoms, and comorbidities as treatment targets that might help clinicians improve quality of life in this population.     

SANE (Structure Assisted NOE Evaluation): An automated model-based approach for NOE assignment

A reliable automated approach for assignment of NOESY spectra would allow more rapid determination of protein structures by NMR. In this paper we describe a semi-automated procedure for complete NOESY assignment (SANE, Structure Assisted NOE Evaluation), coupled to an iterative procedure for NMR structure determination where the user is directly involved. Our method is similar to ARIA [Nilges et al. (1997) J. Mol. Biol., 269, 408–422], but is compatible with the molecular dynamics suites AMBER and DYANA. The method is ideal for systems where an initial model or crystal structure is available, but has also been used successfully for ab initio structure determination. Use of this semi-automated iterative approach assists in the identification of errors in the NOE assignments to short-cut the path to an NMR solution structure.     

GPU accelerated sequence alignment with traceback for GATK HaplotypeCaller

Background

Pairwise sequence alignment is widely used in many biological tools and applications. Existing GPU accelerated implementations mainly focus on calculating optimal alignment score and omit identifying the optimal alignment itself. In GATK HaplotypeCaller (HC), the semi-global pairwise sequence alignment with traceback has so far been difficult to accelerate effectively on GPUs.

Results

We first analyze the characteristics of the semi-global alignment with traceback in GATK HC and then propose a new algorithm that allows for retrieving the optimal alignment efficiently on GPUs. For the first stage, we choose intra-task parallelization model to calculate the position of the optimal alignment score and the backtracking matrix. Moreover, in the first stage, our GPU implementation also records the length of consecutive matches/mismatches in addition to lengths of consecutive insertions and deletions as in the CPU-based implementation. This helps efficiently retrieve the backtracking matrix to obtain the optimal alignment in the second stage.

Conclusions

Experimental results show that our alignment kernel with traceback is up to 80x and 14.14x faster than its CPU counterpart with synthetic datasets and real datasets, respectively. When integrated into GATK HC (alongside a GPU accelerated pair-HMMs forward kernel), the overall acceleration is 2.3x faster than the baseline GATK HC implementation, and 1.34x faster than the GATK HC implementation with the integrated GPU-based pair-HMMs forward algorithm. Although the methods proposed in this paper is to improve the performance of GATK HC, they can also be used in other pairwise alignments and applications.

     

Integrated web visualizations for protein-protein interaction databases

Background

Understanding living systems is crucial for curing diseases. To achieve this task we have to understand biological networks based on protein-protein interactions. Bioinformatics has come up with a great amount of databases and tools that support analysts in exploring protein-protein interactions on an integrated level for knowledge discovery. They provide predictions and correlations, indicate possibilities for future experimental research and fill the gaps to complete the picture of biochemical processes. There are numerous and huge databases of protein-protein interactions used to gain insights into answering some of the many questions of systems biology. Many computational resources integrate interaction data with additional information on molecular background. However, the vast number of diverse Bioinformatics resources poses an obstacle to the goal of understanding. We present a survey of databases that enable the visual analysis of protein networks.

Results

We selected M =10 out of N =53 resources supporting visualization, and we tested against the following set of criteria: interoperability, data integration, quantity of possible interactions, data visualization quality and data coverage. The study reveals differences in usability, visualization features and quality as well as the quantity of interactions. StringDB is the recommended first choice. CPDB presents a comprehensive dataset and IntAct lets the user change the network layout. A comprehensive comparison table is available via web. The supplementary table can be accessed on http://tinyurl.com/PPI-DB-Comparison-2015.

Conclusions

Only some web resources featuring graph visualization can be successfully applied to interactive visual analysis of protein-protein interaction. Study results underline the necessity for further enhancements of visualization integration in biochemical analysis tools. Identified challenges are data comprehensiveness, confidence, interactive feature and visualization maturing.     

Performance and scaling behavior of bioinformatic applications in virtualization environments to create awareness for the efficient use of compute resources

The large amount of biological data available in the current times, makes it necessary to use tools and applications based on sophisticated and efficient algorithms, developed in the area of bioinformatics. Further, access to high performance computing resources is necessary, to achieve results in reasonable time. To speed up applications and utilize available compute resources as efficient as possible, software developers make use of parallelization mechanisms, like multithreading. Many of the available tools in bioinformatics offer multithreading capabilities, but more compute power is not always helpful. In this study we investigated the behavior of well-known applications in bioinformatics, regarding their performance in the terms of scaling, different virtual environments and different datasets with our benchmarking tool suite BOOTABLE. The tool suite includes the tools BBMap, Bowtie2, BWA, Velvet, IDBA, SPAdes, Clustal Omega, MAFFT, SINA and GROMACS. In addition we added an application using the machine learning framework TensorFlow. Machine learning is not directly part of bioinformatics but applied to many biological problems, especially in the context of medical images (X-ray photographs). The mentioned tools have been analyzed in two different virtual environments, a virtual machine environment based on the OpenStack cloud software and in a Docker environment. The gained performance values were compared to a bare-metal setup and among each other. The study reveals, that the used virtual environments produce an overhead in the range of seven to twenty-five percent compared to the bare-metal environment. The scaling measurements showed, that some of the analyzed tools do not benefit from using larger amounts of computing resources, whereas others showed an almost linear scaling behavior. The findings of this study have been generalized as far as possible and should help users to find the best amount of resources for their analysis. Further, the results provide valuable information for resource providers to handle their resources as efficiently as possible and raise the user community’s awareness of the efficient usage of computing resources.     

The Values of Combined and Sub-Stratified Imaging Scores with Ultrasonography and Mammography in Breast Cancer Subtypes

ResultsThese selected cases were evaluated by the “5-point score.” MG, US, and combined and sub-stratified imaging assessments all revealed statistically significant (P < 0.001) incidence of malignancy. The sensitivity was increased in the combined imaging score (99.8%), and the specificity was increased in the sub-stratified combined score (75.4%). In the sub-stratified combined imaging assessment, all BCS can be classified with higher scores (abnormality hierarchy), and luminal B subtype showed the most salient result (hierarchy: higher, 95%; lower, 5%).ConclusionsCombined and sub-stratified imaging assessments can increase sensitivity and specificity of breast cancer diagnosis, respectively, and Luminal B subtype shows the best identification by sub-stratified combined imaging scoring.     

园艺植物分子育种相关生物信息资源及其应用

园艺植物分子育种中,生物信息技术是一项新技术.GenBank、EMBL、DDBJ、Swiss-Prot等数据库及其序列查询系统、序列比对软件和序列提交软件是园艺植物分子育种中的重要生物信息资源.本文综述了这些生物信息资源,以及它们在克隆新基因、预测新序列功能、鉴定种质资源和进行系谱分析等方面的应用.     

园艺植物分子育种相关生物信息资源及其应用

园艺植物分子育种中, 生物信息技术是一项新技术。GenBank、EMBL、DDBJ、Swiss-Prot等数据库及其序列查询系统、序列比对软件和序列提交软件是园艺植物分子育种中的重要生物信息资源。本文综述了这些生物信息资源, 以及它们在克隆新基因、预测新序列功能、鉴定种质资源和进行系谱分析等方面的应用。     

Mining cancer biology through bioinformatic analysis of proteomic data

ABSTRACT

Introduction: Discovery proteomics for cancer research generates complex datasets of diagnostic, prognostic, and therapeutic significance in human cancer. With the advent of high-resolution mass spectrometers, able to identify thousands of proteins in complex biological samples, only the application of bioinformatics can lead to the interpretation of data which can be relevant for cancer research.

Areas covered: Here, we give an overview of the current bioinformatic tools used in cancer proteomics. Moreover, we describe their applications in cancer proteomics studies of cell lines, serum, and tissues, highlighting recent results and critically evaluating their outcomes.

Expert opinion: The use of bioinformatic tools is a fundamental step in order to manage the large amount of proteins (from hundreds to thousands) that can be identified and quantified in a cancer biological samples by proteomics. To handle this challenge and obtain useful data for translational medicine, it is important the combined use of different bioinformatic tools. Moreover, a particular attention to the global experimental design, and the integration of multidisciplinary skills are essential for best setting of tool parameters and best interpretation of bioinformatics output.     

Thermodynamic resource indicators in LCA: a case study on the titania produced in Panzhihua city, southwest China

Purpose

While life cycle assessment (LCA) has standardized methods for assessing emission impacts, some comparable methods for the accounting or impact assessment of resource use exist, but are not as mature or standardized. This study contributes to the existing research by offering a comprehensive comparison of the similarities and differences of different resource indicators, in particular those based on thermodynamics, and testing them in a case study on titania (titanium dioxide pigment) produced in Panzhihua city, southwest China.

Materials and methods

The system boundary for resource indicators is defined using a thermodynamic hierarchy at four levels, and the case data for titania also follow that hierarchy. Seven resource indicators are applied. Four are thermodynamics-based??cumulative energy demand (CED), solar energy demand (SED), cumulative exergy demand (CExD), and cumulative exergy extraction from the natural environment (CEENE)??and three have different backgrounds: abiotic resource depletion potential, environmental priority strategies, and eco-indicator 99. Inventory data for the foreground system has been collected through on-site interviews and visits. Background inventory data are from the database ecoinvent v2.2. Characterizations factors are based on the CML-IA database covering all major methods. Computations are with the CMLCA software.

Results and discussion

The scores of resource indicators of the chloride route for titania system are lower than that of the sulfate route by 10?C35?%, except in terms of SED. Within the four thermodynamic indicators for resources, CED, CExD, and CEENE have similar scores, while their scores are five orders of magnitude lower than the SED score. Atmospheric resources do not contribute to the SED or CEEND score. Land resources account for a negligible percentage to the SED score and a small percentage to the CEENE score. Non-renewable resources have a dominant contribution to all seven resource indicators. The global production of titania would account for 0.12 and 0.14?% of the total anthropogenic non-renewable resource demand in terms of energy and exergy, respectively.

Conclusions

First, we demonstrate the feasibility of thermodynamic resource indicators. We recommend CEENE as the most appropriate one within the four thermodynamic resource indicators for accounting and characterizing resource use. Regarding the case study on the titania produced in China, all the resource indicators except SED show that the sulfate route demands more resource use than the chloride route.     

Understanding of mouse and human bladder at single‐cell resolution: integrated analysis of trajectory and cell‐cell interactive networks based on multiple scRNA‐seq datasets

ObjectivesTo elaborately decipher the mouse and human bladders at single‐cell levels.Materials and MethodsWe collected more than 50,000 cells from multiple datasets and created, up to date, the largest integrated bladder datasets. Pseudotime trajectory of urothelium and interstitial cells, as well as dynamic cell‐cell interactions, was investigated. Biological activity scores and different roles of signaling pathways between certain cell clusters were also identified.ResultsThe glucose score was significantly high in most urothelial cells, while the score of H3 acetylation was roughly equally distributed across all cell types. Several genes via a pseudotime pattern in mouse (Car3, Dkk2, Tnc, etc.) and human (FBLN1, S100A10, etc.) were discovered. S100A6, TMSB4X, and typical uroplakin genes seemed as shared pseudotime genes for urothelial cells in both human and mouse datasets. In combinational mouse (n = 16,688) and human (n = 22,080) bladders, we verified 1,330 and 1,449 interactive ligand‐receptor pairs, respectively. The distinct incoming and outgoing signaling was significantly associated with specific cell types. Collagen was the strongest signal from fibroblasts to urothelial basal cells in mouse, while laminin pathway for urothelial basal cells to smooth muscle cells (SMCs) in human. Fibronectin 1 pathway was intensely sent by myofibroblasts, received by urothelial cells, and almost exclusively mediated by SMCs in mouse bladder. Interestingly, the cell cluster of SMCs 2 was the dominant sender and mediator for Notch signaling in the human bladder, while SMCs 1 was not. The expression of integrin superfamily (the most common communicative pairs) was depicted, and their co‐expression patterns were located in certain cell types (eg, Itgb1 and Itgb4 in mouse and human basal cells).ConclusionsThis study provides a complete interpretation of the normal bladder at single‐cell levels, offering an in‐depth resource and foundation for future research.