Construction of a series of vectors for high throughput cloning and expression screening of membrane proteins from <Emphasis Type="Italic">Mycobacterium tuberculosis</Emphasis>

Background  

One of the major challenges for membrane protein structural genomics is establishing high-throughput cloning and expression screening methods to obtain enough purified protein in a homogeneous preparation for structural and functional studies. Here a series of ligation independent cloning based vectors were constructed to address this challenge.     

web cellHTS2: A web-application for the analysis of high-throughput screening data

Background  

The analysis of high-throughput screening data sets is an expanding field in bioinformatics. High-throughput screens by RNAi generate large primary data sets which need to be analyzed and annotated to identify relevant phenotypic hits. Large-scale RNAi screens are frequently used to identify novel factors that influence a broad range of cellular processes, including signaling pathway activity, cell proliferation, and host cell infection. Here, we present a web-based application utility for the end-to-end analysis of large cell-based screening experiments by cellHTS2.     

Scoring functions and enrichment: a case study on Hsp90

Background  

The need for fast and accurate scoring functions has been driven by the increased use of in silico virtual screening twinned with high-throughput screening as a method to rapidly identify potential candidates in the early stages of drug development. We examine the ability of some the most common scoring functions (GOLD, ChemScore, DOCK, PMF, BLEEP and Consensus) to discriminate correctly and efficiently between active and non-active compounds among a library of ~3,600 diverse decoy compounds in a virtual screening experiment against heat shock protein 90 (Hsp90).     

Automated assay for screening the enzymatic release of reducing sugars from micronized biomass

Background  

To reduce the production cost of bioethanol obtained from fermentation of the sugars provided by degradation of lignocellulosic biomass (i.e., second generation bioethanol), it is necessary to screen for new enzymes endowed with more efficient biomass degrading properties. This demands the set-up of high-throughput screening methods. Several methods have been devised all using microplates in the industrial SBS format. Although this size reduction and standardization has greatly improved the screening process, the published methods comprise one or more manual steps that seriously decrease throughput. Therefore, we worked to devise a screening method devoid of any manual steps.     

A high-throughput chemically induced inflammation assay in zebrafish

Background  

Studies on innate immunity have benefited from the introduction of zebrafish as a model system. Transgenic fish expressing fluorescent proteins in leukocyte populations allow direct, quantitative visualization of an inflammatory response in vivo. It has been proposed that this animal model can be used for high-throughput screens aimed at the identification of novel immunomodulatory lead compounds. However, current assays require invasive manipulation of fish individually, thus preventing high-content screening.     

Construction of siRNA/miRNA expression vectors based on a one-step PCR process

Background  

RNA interference (RNAi) has become a powerful means for silencing target gene expression in mammalian cells and is envisioned to be useful in therapeutic approaches to human disease. In recent years, high-throughput, genome-wide screening of siRNA/miRNA libraries has emerged as a desirable approach. Current methods for constructing siRNA/miRNA expression vectors require the synthesis of long oligonucleotides, which is costly and suffers from mutation problems.     

A modified TILLING approach to detect induced mutations in tetraploid and hexaploid wheat

Background  

Wheat (Triticum ssp.) is an important food source for humans in many regions around the world. However, the ability to understand and modify gene function for crop improvement is hindered by the lack of available genomic resources. TILLING is a powerful reverse genetics approach that combines chemical mutagenesis with a high-throughput screen for mutations. Wheat is specially well-suited for TILLING due to the high mutation densities tolerated by polyploids, which allow for very efficient screens. Despite this, few TILLING populations are currently available. In addition, current TILLING screening protocols require high-throughput genotyping platforms, limiting their use.     

Pan-genome sequence analysis using Panseq: an online tool for the rapid analysis of core and accessory genomic regions

Background  

The pan-genome of a bacterial species consists of a core and an accessory gene pool. The accessory genome is thought to be an important source of genetic variability in bacterial populations and is gained through lateral gene transfer, allowing subpopulations of bacteria to better adapt to specific niches. Low-cost and high-throughput sequencing platforms have created an exponential increase in genome sequence data and an opportunity to study the pan-genomes of many bacterial species. In this study, we describe a new online pan-genome sequence analysis program, Panseq.     

PCR-based generation of shRNA libraries from cDNAs

Background  

The use of small interfering RNAs (siRNAs) to silence target gene expression has greatly facilitated mammalian genetic analysis by generating loss-of-function mutants. In recent years, high-throughput, genome-wide screening of siRNA libraries has emerged as a viable approach. Two different methods have been used to generate short hairpin RNA (shRNA) libraries; one is to use chemically synthesized oligonucleotides, and the other is to convert complementary DNAs (cDNAs) into shRNA cassettes enzymatically. The high cost of chemical synthesis and the low efficiency of the enzymatic approach have hampered the widespread use of screening with shRNA libraries.     

<Emphasis Type="Italic">In silico</Emphasis> panning for a non-competitive peptide inhibitor

Background  

Peptide ligands have tremendous therapeutic potential as efficacious drugs. Currently, more than 40 peptides are available in the market for a drug. However, since costly and time-consuming synthesis procedures represent a problem for high-throughput screening, novel procedures to reduce the time and labor involved in screening peptide ligands are required. We propose the novel approach of 'in silico panning' which consists of a two-stage screening, involving affinity selection by docking simulation and evolution of the peptide ligand using genetic algorithms (GAs). In silico panning was successfully applied to the selection of peptide inhibitor for water-soluble quinoprotein glucose dehydrogenase (PQQGDH).     

Inferring modules from human protein interactome classes

Background  

The integration of protein-protein interaction networks derived from high-throughput screening approaches and complementary sources is a key topic in systems biology. Although integration of protein interaction data is conventionally performed, the effects of this procedure on the result of network analyses has not been examined yet. In particular, in order to optimize the fusion of heterogeneous interaction datasets, it is crucial to consider not only their degree of coverage and accuracy, but also their mutual dependencies and additional salient features.     

A family of E. coli expression vectors for laboratory scale and high throughput soluble protein production

Background  

In the past few years, both automated and manual high-throughput protein expression and purification has become an accessible means to rapidly screen and produce soluble proteins for structural and functional studies. However, many of the commercial vectors encoding different solubility tags require different cloning and purification steps for each vector, considerably slowing down expression screening. We have developed a set of E. coli expression vectors with different solubility tags that allow for parallel cloning from a single PCR product and can be purified using the same protocol.     

Simultaneous analysis of large-scale RNAi screens for pathogen entry

Background

Large-scale RNAi screening has become an important technology for identifying genes involved in biological processes of interest. However, the quality of large-scale RNAi screening is often deteriorated by off-targets effects. In order to find statistically significant effector genes for pathogen entry, we systematically analyzed entry pathways in human host cells for eight pathogens using image-based kinome-wide siRNA screens with siRNAs from three vendors. We propose a Parallel Mixed Model (PMM) approach that simultaneously analyzes several non-identical screens performed with the same RNAi libraries.

Results

We show that PMM gains statistical power for hit detection due to parallel screening. PMM allows incorporating siRNA weights that can be assigned according to available information on RNAi quality. Moreover, PMM is able to estimate a sharedness score that can be used to focus follow-up efforts on generic or specific gene regulators. By fitting a PMM model to our data, we found several novel hit genes for most of the pathogens studied.

Conclusions

Our results show parallel RNAi screening can improve the results of individual screens. This is currently particularly interesting when large-scale parallel datasets are becoming more and more publicly available. Our comprehensive siRNA dataset provides a public, freely available resource for further statistical and biological analyses in the high-content, high-throughput siRNA screening field.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-1162) contains supplementary material, which is available to authorized users.     

Robust,small-scale cultivation platform for <Emphasis Type="Italic">Streptomyces coelicolor</Emphasis>

Background  

For fermentation process and strain improvement, where one wants to screen a large number of conditions and strains, robust and scalable high-throughput cultivation systems are crucial. Often, the time lag between bench-scale cultivations to production largely depends on approximate estimation of scalable physiological traits. Microtiter plate (MTP) based screening platforms have lately become an attractive alternative to shake flasks mainly because of the ease of automation. However, there are very few reports on applications for filamentous organisms; as well as efforts towards systematic validation of physiological behavior compared to larger scale are sparse. Moreover, available small-scale screening approaches are typically constrained by evaluating only an end point snapshot of phenotypes.     

Inferring gene expression dynamics via functional regression analysis

Background

The recent emergence of high-throughput automated image acquisition technologies has forever changed how cell biologists collect and analyze data. Historically, the interpretation of cellular phenotypes in different experimental conditions has been dependent upon the expert opinions of well-trained biologists. Such qualitative analysis is particularly effective in detecting subtle, but important, deviations in phenotypes. However, while the rapid and continuing development of automated microscope-based technologies now facilitates the acquisition of trillions of cells in thousands of diverse experimental conditions, such as in the context of RNA interference (RNAi) or small-molecule screens, the massive size of these datasets precludes human analysis. Thus, the development of automated methods which aim to identify novel and biological relevant phenotypes online is one of the major challenges in high-throughput image-based screening. Ideally, phenotype discovery methods should be designed to utilize prior/existing information and tackle three challenging tasks, i.e. restoring pre-defined biological meaningful phenotypes, differentiating novel phenotypes from known ones and clarifying novel phenotypes from each other. Arbitrarily extracted information causes biased analysis, while combining the complete existing datasets with each new image is intractable in high-throughput screens.

Results

Here we present the design and implementation of a novel and robust online phenotype discovery method with broad applicability that can be used in diverse experimental contexts, especially high-throughput RNAi screens. This method features phenotype modelling and iterative cluster merging using improved gap statistics. A Gaussian Mixture Model (GMM) is employed to estimate the distribution of each existing phenotype, and then used as reference distribution in gap statistics. This method is broadly applicable to a number of different types of image-based datasets derived from a wide spectrum of experimental conditions and is suitable to adaptively process new images which are continuously added to existing datasets. Validations were carried out on different dataset, including published RNAi screening using Drosophila embryos [Additional files 1, 2], dataset for cell cycle phase identification using HeLa cells [Additional files 1, 3, 4] and synthetic dataset using polygons, our methods tackled three aforementioned tasks effectively with an accuracy range of 85%–90%. When our method is implemented in the context of a Drosophila genome-scale RNAi image-based screening of cultured cells aimed to identifying the contribution of individual genes towards the regulation of cell-shape, it efficiently discovers meaningful new phenotypes and provides novel biological insight. We also propose a two-step procedure to modify the novelty detection method based on one-class SVM, so that it can be used to online phenotype discovery. In different conditions, we compared the SVM based method with our method using various datasets and our methods consistently outperformed SVM based method in at least two of three tasks by 2% to 5%. These results demonstrate that our methods can be used to better identify novel phenotypes in image-based datasets from a wide range of conditions and organisms.

Conclusion

We demonstrate that our method can detect various novel phenotypes effectively in complex datasets. Experiment results also validate that our method performs consistently under different order of image input, variation of starting conditions including the number and composition of existing phenotypes, and dataset from different screens. In our findings, the proposed method is suitable for online phenotype discovery in diverse high-throughput image-based genetic and chemical screens.     

Limitations in a frataxin knockdown cell model for Friedreich ataxia in a high-throughput drug screen

Background  

Pharmacological high-throughput screening (HTS) represents a powerful strategy for drug discovery in genetic diseases, particularly when the full spectrum of pathological dysfunctions remains unclear, such as in Friedreich ataxia (FRDA). FRDA, the most common recessive ataxia, results from a generalized deficiency of mitochondrial and cytosolic iron-sulfur cluster (ISC) proteins activity, due to a partial loss of frataxin function, a mitochondrial protein proposed to function as an iron-chaperone for ISC biosynthesis. In the absence of measurable catalytic function for frataxin, a cell-based assay is required for HTS assay.     

QSRA – a quality-value guided de novo short read assembler

Background  

New rapid high-throughput sequencing technologies have sparked the creation of a new class of assembler. Since all high-throughput sequencing platforms incorporate errors in their output, short-read assemblers must be designed to account for this error while utilizing all available data.     

A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model

Background  

Bioactivity profiling using high-throughput in vitro assays can reduce the cost and time required for toxicological screening of environmental chemicals and can also reduce the need for animal testing. Several public efforts are aimed at discovering patterns or classifiers in high-dimensional bioactivity space that predict tissue, organ or whole animal toxicological endpoints. Supervised machine learning is a powerful approach to discover combinatorial relationships in complex in vitro/in vivo datasets. We present a novel model to simulate complex chemical-toxicology data sets and use this model to evaluate the relative performance of different machine learning (ML) methods.