Integrating a functional proteomic approach into the target discovery process

Functional proteomics is a promising technique for the rational identification of novel therapeutic targets by elucidation of the function of newly identified proteins in disease-relevant cellular pathways. Of the recently described high-throughput approaches for analyzing protein-protein interactions, the yeast two-hybrid (Y2H) system has turned out to be one of the most suitable for genome-wide analysis. However, this system presents a challenging technical problem: the high prevalence of false positives and false negatives in datasets due to intrinsic limitations of the technology and the use of a high-throughput, genetic assay. We discuss here the different experimental strategies applied to Y2H assays, their general limitations and advantages. We also address the issue of the contribution of protein interaction mapping to functional biology, especially when combined with complementary genomic and proteomic analyses. Finally, we illustrate how the combination of protein interaction maps with relevant functional assays can provide biological support to large-scale protein interaction datasets and contribute to the identification and validation of potential therapeutic targets.     

The McGill-Mouse-Miniscope platform: A standardized approach for high-throughput imaging of neuronal dynamics during behavior

Understanding the rules that govern neuronal dynamics throughout the brain to subserve behavior and cognition remains one of the biggest challenges in neuroscience research. Recent technical advances enable the recording of increasingly larger neuronal populations to produce increasingly more sophisticated datasets. Despite bold and important open-science and data-sharing policies, these datasets tend to include unique data acquisition methods, behaviors, and file structures. Discrepancies between experimental protocols present key challenges in comparing data between laboratories and across different brain regions and species. Here, we discuss our recent efforts to create a standardized and high-throughput research platform to address these issues. The McGill-Mouse-Miniscope (M3) platform is an initiative to combine miniscope calcium imaging with standardized touchscreen-based animal behavioral testing. The goal is to curate an open-source and standardized framework for acquiring, analyzing, and accessing high-quality data of the neuronal dynamics that underly cognition throughout the brain in mice, marmosets, and models of disease. We end with a discussion of future developments and a call for users to adopt this standardized approach.     

Quality assessment and interference detection in targeted mass spectrometry data using machine learning

Advances in the field of targeted proteomics and mass spectrometry have significantly improved assay sensitivity and multiplexing capacity. The high-throughput nature of targeted proteomics experiments has increased the rate of data production, which requires development of novel analytical tools to keep up with data processing demand. Currently, development and validation of targeted mass spectrometry assays require manual inspection of chromatographic peaks from large datasets to ensure quality, a process that is time consuming, prone to inter- and intra-operator variability and limits the efficiency of data interpretation from targeted proteomics analyses. To address this challenge, we have developed TargetedMSQC, an R package that facilitates quality control and verification of chromatographic peaks from targeted proteomics datasets. This tool calculates metrics to quantify several quality aspects of a chromatographic peak, e.g. symmetry, jaggedness and modality, co-elution and shape similarity of monitored transitions in a peak group, as well as the consistency of transitions’ ratios between endogenous analytes and isotopically labeled internal standards and consistency of retention time across multiple runs. The algorithm takes advantage of supervised machine learning to identify peaks with interference or poor chromatography based on a set of peaks that have been annotated by an expert analyst. Using TargetedMSQC to analyze targeted proteomics data reduces the time spent on manual inspection of peaks and improves both speed and accuracy of interference detection. Additionally, by allowing the analysts to customize the tool for application on different datasets, TargetedMSQC gives the users the flexibility to define the acceptable quality for specific datasets. Furthermore, automated and quantitative assessment of peak quality offers a more objective and systematic framework for high throughput analysis of targeted mass spectrometry assay datasets and is a step towards more robust and faster assay implementation.     

Increasing confidence of protein interactomes using network topological metrics

MOTIVATION: Experimental limitations in high-throughput protein-protein interaction detection methods have resulted in low quality interaction datasets that contained sizable fractions of false positives and false negatives. Small-scale, focused experiments are then needed to complement the high-throughput methods to extract true protein interactions. However, the naturally vast interactomes would require much more scalable approaches. RESULTS: We describe a novel method called IRAP* as a computational complement for repurification of the highly erroneous experimentally derived protein interactomes. Our method involves an iterative process of removing interactions that are confidently identified as false positives and adding interactions detected as false negatives into the interactomes. Identification of both false positives and false negatives are performed in IRAP* using interaction confidence measures based on network topological metrics. Potential false positives are identified amongst the detected interactions as those with very low computed confidence values, while potential false negatives are discovered as the undetected interactions with high computed confidence values. Our results from applying IRAP* on large-scale interaction datasets generated by the popular yeast-two-hybrid assays for yeast, fruit fly and worm showed that the computationally repurified interaction datasets contained potentially lower fractions of false positive and false negative errors based on functional homogeneity. AVAILABILITY: The confidence indices for PPIs in yeast, fruit fly and worm as computed by our method can be found at our website http://www.comp.nus.edu.sg/~chenjin/fpfn.     

Compression of Structured High-Throughput Sequencing Data

Large biological datasets are being produced at a rapid pace and create substantial storage challenges, particularly in the domain of high-throughput sequencing (HTS). Most approaches currently used to store HTS data are either unable to quickly adapt to the requirements of new sequencing or analysis methods (because they do not support schema evolution), or fail to provide state of the art compression of the datasets. We have devised new approaches to store HTS data that support seamless data schema evolution and compress datasets substantially better than existing approaches. Building on these new approaches, we discuss and demonstrate how a multi-tier data organization can dramatically reduce the storage, computational and network burden of collecting, analyzing, and archiving large sequencing datasets. For instance, we show that spliced RNA-Seq alignments can be stored in less than 4% the size of a BAM file with perfect data fidelity. Compared to the previous compression state of the art, these methods reduce dataset size more than 40% when storing exome, gene expression or DNA methylation datasets. The approaches have been integrated in a comprehensive suite of software tools (http://goby.campagnelab.org) that support common analyses for a range of high-throughput sequencing assays.     

DAVID gene ID conversion tool

Our current biological knowledge is spread over many independent bioinformatics databases where many different types of gene and protein identifiers are used. The heterogeneous and redundant nature of these identifiers limits data analysis across different bioinformatics resources. It is an even more serious bottleneck of data analysis for larger datasets, such as gene lists derived from microarray and proteomic experiments. The DAVID Gene ID Conversion Tool (DICT), a web-based application, is able to convert user's input gene or gene product identifiers from one type to another in a more comprehensive and high-throughput manner with a uniquely enhanced ID-ID mapping database.     

Linking phenotypic correlations from a diverse set of laboratory tests to field behaviors in the crayfish,Orconectes virilis

The presence of phenotypic behavioral correlations and their connection to fitness consequences of organisms have received considerable debate within the literature. Yet, little work has been carried out to connect any behavioral correlates found within a set of laboratory studies to natural behavior observed under complex environmental conditions. To help fill this gap, individual crayfish, collected from the same local population, completed five different behavioral assays in a laboratory setting in a random order. These data were used to reveal any possible correlations for behavioral scores across all of the laboratory tests. Subsequently, these same individuals were placed into the field and video recorded for 24 hr. A separate set of field behaviors, related to the laboratory assays, were quantified from the field videos. The normalized laboratory and field behaviors were used in three stepwise statistical analyses. First, normalized data were loaded into a PCA to generate a priori hypotheses on potential behavioral correlates. These hypotheses were subsequently tested using general multiple linear regression. Finally, structural equation modeling was performed to elucidate any behavioral modules from the laboratory assays that correlated with behavioral patterns present from the fieldwork. Three laboratory‐based behavioral modules were connected to three separate field assays: exploration–avoidance, bold–shy, and aggressiveness. Yet, some behaviors exhibited in the laboratory assays were uncorrelated with any behaviors found in the field and vice versa. Results from this study provide evidence that although many different behavioral correlates may exist within laboratory settings, these same modules may not translate directly into predicting behavior under natural settings.     

Measuring behavioral and endocrine responses to novelty stress in adult zebrafish

Several behavioral assays are currently used for high-throughput neurophenotyping and screening of genetic mutations and psychotropic drugs in zebrafish (Danio rerio). In this protocol, we describe a battery of two assays to characterize anxiety-related behavioral and endocrine phenotypes in adult zebrafish. Here, we detail how to use the 'novel tank' test to assess behavioral indices of anxiety (including reduced exploration, increased freezing behavior and erratic movement), which are quantifiable using manual registration and computer-aided video-tracking analyses. In addition, we describe how to analyze whole-body zebrafish cortisol concentrations that correspond to their behavior in the novel tank test. This protocol is an easy, inexpensive and effective alternative to other methods of measuring stress responses in zebrafish, thus enabling the rapid acquisition and analysis of large amounts of data. As will be shown here, fish anxiety-like behavior can be either attenuated or exaggerated depending on stress or drug exposure, with cortisol levels generally expected to parallel anxiety behaviors. This protocol can be completed over the course of 2 d, with a variable testing duration depending on the number of fish used.     

High-throughput single-molecule studies of protein–DNA interactions

Fluorescence and force-based single-molecule studies of protein–nucleic acid interactions continue to shed critical insights into many aspects of DNA and RNA processing. As single-molecule assays are inherently low-throughput, obtaining statistically relevant datasets remains a major challenge. Additionally, most fluorescence-based single-molecule particle-tracking assays are limited to observing fluorescent proteins that are in the low-nanomolar range, as spurious background signals predominate at higher fluorophore concentrations. These technical limitations have traditionally limited the types of questions that could be addressed via single-molecule methods. In this review, we describe new approaches for high-throughput and high-concentration single-molecule biochemical studies. We conclude with a discussion of outstanding challenges for the single-molecule biologist and how these challenges can be tackled to further approach the biochemical complexity of the cell.     

Identifying common prognostic factors in genomic cancer studies: A novel index for censored outcomes

Background  

With the growing number of public repositories for high-throughput genomic data, it is of great interest to combine the results produced by independent research groups. Such a combination allows the identification of common genomic factors across multiple cancer types and provides new insights into the disease process. In the framework of the proportional hazards model, classical procedures, which consist of ranking genes according to the estimated hazard ratio or the p-value obtained from a test statistic of no association between survival and gene expression level, are not suitable for gene selection across multiple genomic datasets with different sample sizes. We propose a novel index for identifying genes with a common effect across heterogeneous genomic studies designed to remain stable whatever the sample size and which has a straightforward interpretation in terms of the percentage of separability between patients according to their survival times and gene expression measurements.     

High-throughput approaches to dissecting MAPK signaling pathways

With the development of genome-wide RNAi libraries, it is now possible to screen for novel components of mitogen-activated protein kinase (MAPK) pathways in cell culture. Although genetic dissection in model organisms and biochemical approaches in mammalian cells have been successful in identifying the core signaling cassettes of these pathways, high-throughput assays can yield unbiased, functional genomic insight into pathway regulation. We describe general high-throughput approaches to assaying MAPK signaling and the receptor tyrosine kinase (RTK)/extracellular signal-regulated kinase (ERK) pathway in particular using a phospho-specific antibody-based readout of pathway activity. We also provide examples of secondary validation screens and methods for managing large datasets for future in vivo functional characterization.     

利用红外相机建立野生水鹿行为谱及PAE编码系统

动物行为谱是动物行为学研究的基础。2015年1月至2016年6月,在四川卧龙自然保护区,利用PAE编码分类系统并结合红外相机所获得的照片和视频数据开展水鹿(Rusa unicolor)行为谱的研究,建立了野生水鹿行为谱及其PAE编码系统。本研究共分辨并记录了野生水鹿的7种姿势,63种动作和74种行为,基本涵盖了水鹿的主要行为,并区别了各种行为在成年雄性、成年雌性和亚成体之间的相对发生频次,分析水鹿的具体行为与环境之间的关联。通过建立野生水鹿的行为谱,能为更深入地开展水鹿行为生态学研究提供基础信息,也为水鹿的保护与管理提供依据。该研究可以推广到其他不易直接观测的野生动物的行为学研究中。     

Inferring mechanisms of compensation from E-MAP and SGA data using local search algorithms for max cut

A new method based on a mathematically natural local search framework for max cut is developed to uncover functionally coherent module and BPM motifs in high-throughput genetic interaction data. Unlike previous methods, which also consider physical protein-protein interaction data, our method utilizes genetic interaction data only; this becomes increasingly important as high-throughput genetic interaction data is becoming available in settings where less is known about physical interaction data. We compare modules and BPMs obtained to previous methods and across different datasets. Despite needing no physical interaction information, the BPMs produced by our method are competitive with previous methods. Biological findings include a suggested global role for the prefoldin complex and a SWR subcomplex in pathway buffering in the budding yeast interactome.     

Improved Inference of Taxonomic Richness from Environmental DNA

Accurate estimation of biological diversity in environmental DNA samples using high-throughput amplicon pyrosequencing must account for errors generated by PCR and sequencing. We describe a novel approach to distinguish the underlying sequence diversity in environmental DNA samples from errors that uses information on the abundance distribution of similar sequences across independent samples, as well as the frequency and diversity of sequences within individual samples. We have further refined this approach into a bioinformatics pipeline, Amplicon Pyrosequence Denoising Program (APDP) that is able to process raw sequence datasets into a set of validated sequences in formats compatible with commonly used downstream analyses packages. We demonstrate, by sequencing complex environmental samples and mock communities, that APDP is effective for removing errors from deeply sequenced datasets comprising biological and technical replicates, and can efficiently denoise single-sample datasets. APDP provides more conservative diversity estimates for complex datasets than other approaches; however, for some applications this may provide a more accurate and appropriate level of resolution, and result in greater confidence that returned sequences reflect the diversity of the underlying sample.     

Automated SNP genotype clustering algorithm to improve data completeness in high-throughput SNP genotyping datasets from custom arrays

High-throughput SNP genotyping platforms use automated genotype calling algorithms to assign genotypes. While these algorithms work efficiently for individual platforms, they are not compatible with other platforms, and have individual biases that result in missed genotype calls. Here we present data on the use of a second complementary SNP genotype clustering algorithm. The algorithm was originally designed for individual fluorescent SNP genotyping assays, and has been optimized to permit the clustering of large datasets generated from custom-designed Affymetrix SNP panels. In an analysis of data from a 3K array genotyped on 1,560 samples, the additional analysis increased the overall number of genotypes by over 45,000, significantly improving the completeness of the experimental data. This analysis suggests that the use of multiple genotype calling algorithms may be advisable in high-throughput SNP genotyping experiments. The software is written in Perl and is available from the corresponding author.     

Automated SNP Genotype Clustering Algorithm to Improve Data Completeness in High-Throughput SNP Genotyping Datasets from Custom Arrays

High-throughput SNP genotyping platforms use automated genotype calling algo- rithms to assign genotypes. While these algorithms work efficiently for individual platforms, they are not compatible with other platforms, and have individual biases that result in missed genotype calls. Here we present data on the use of a second complementary SNP genotype clustering algorithm. The algorithm was originally designed for individual fluorescent SNP genotyping assays, and has been opti- mized to permit the clustering of large datasets generated from custom-designed Affymetrix SNP panels. In an analysis of data from a 3K array genotyped on 1,560 samples, the additional analysis increased the overall number of genotypes by over 45,000, significantly improving the completeness of the experimental data. This analysis suggests that the use of multiple genotype calling algorithms may be ad- visable in high-throughput SNP genotyping experiments. The software is written in Perl and is available from the corresponding author.     

Automated SNP Genotype Clustering Algorithm to Improve Data Completeness in High-Throughput SNP Genotyping Datasets from Custom Arrays

High-throughput SNP genotyping platforms use automated genotype calling algo- rithms to assign genotypes. While these algorithms work efficiently for individual platforms, they are not compatible with other platforms, and have individual biases that result in missed genotype calls. Here we present data on the use of a second complementary SNP genotype clustering algorithm. The algorithm was originally designed for individual fluorescent SNP genotyping assays, and has been opti- mized to permit the clustering of large datasets generated from custom-designed Affymetrix SNP panels. In an analysis of data from a 3K array genotyped on 1,560 samples, the additional analysis increased the overall number of genotypes by over 45,000, significantly improving the completeness of the experimental data. This analysis suggests that the use of multiple genotype calling algorithms may be ad- visable in high-throughput SNP genotyping experiments. The software is written in Perl and is available from the corresponding author.