WebArray: an online platform for microarray data analysis

Background  

Many cutting-edge microarray analysis tools and algorithms, including commonly used limma and affy packages in Bioconductor, need sophisticated knowledge of mathematics, statistics and computer skills for implementation. Commercially available software can provide a user-friendly interface at considerable cost. To facilitate the use of these tools for microarray data analysis on an open platform we developed an online microarray data analysis platform, WebArray, for bench biologists to utilize these tools to explore data from single/dual color microarray experiments.     

Spatial normalization of array-CGH data

Background  

Array-based comparative genomic hybridization (array-CGH) is a recently developed technique for analyzing changes in DNA copy number. As in all microarray analyses, normalization is required to correct for experimental artifacts while preserving the true biological signal. We investigated various sources of systematic variation in array-CGH data and identified two distinct types of spatial effect of no biological relevance as the predominant experimental artifacts: continuous spatial gradients and local spatial bias. Local spatial bias affects a large proportion of arrays, and has not previously been considered in array-CGH experiments.     

On the brink between extinction and persistence

   

The nature of size fluctuations is crucial in forecasting future population persistence, independently of whether the variability stems from external forces or from the dynamics of the population renewal process. The risk of intercepting zero is highly dependent on the way the variance of the population size relates to its mean. The minimum population size required for a population not to go extinct can be determined by a scaling equation relating the variance to the arithmetic mean. By the use of a derived expression for the harmonic mean defined by the parameters of the scaling equation we show how it is possible to separate the domains of persistence from those of extinction and to facilitate the identification of populations on the brink of extinction.     

Logistic regression over encrypted data from fully homomorphic encryption

Background

One of the tasks in the 2017 iDASH secure genome analysis competition was to enable training of logistic regression models over encrypted genomic data. More precisely, given a list of approximately 1500 patient records, each with 18 binary features containing information on specific mutations, the idea was for the data holder to encrypt the records using homomorphic encryption, and send them to an untrusted cloud for storage. The cloud could then homomorphically apply a training algorithm on the encrypted data to obtain an encrypted logistic regression model, which can be sent to the data holder for decryption. In this way, the data holder could successfully outsource the training process without revealing either her sensitive data, or the trained model, to the cloud.

Methods

Our solution to this problem has several novelties: we use a multi-bit plaintext space in fully homomorphic encryption together with fixed point number encoding; we combine bootstrapping in fully homomorphic encryption with a scaling operation in fixed point arithmetic; we use a minimax polynomial approximation to the sigmoid function and the 1-bit gradient descent method to reduce the plaintext growth in the training process.

Results

Our algorithm for training over encrypted data takes 0.4–3.2 hours per iteration of gradient descent.

Conclusions

We demonstrate the feasibility but high computational cost of training over encrypted data. On the other hand, our method can guarantee the highest level of data privacy in critical applications.

     

Brief report: Lactobacillus bulgaricus GLB44 (Proviotic™) plus esomeprazole for Helicobacter pylori eradication: A pilot study

Background

Recent studies of Lactobacillus delbrueckii subsp. bulgaricus GLB44 plus a proton‐pump inhibitor (PPI) reported cures of more than 90% of patients with active Helicobacter pylori infections.

Aim

To confirm the high H. pylori cure rates reported previously.

Method

A pilot study was done in healthy H. pylori‐infected volunteers using 3‐gram sachet (3 billion cells) of L. delbrueckii GLB44 plus 22.3 mg of esomeprazole b.i.d., for 14 days. The result was determined by urea breath testing 4 weeks after therapy. Stopping rules required for ending enrollment if less than 3 of the first 10 subjects were cured.

Results

Nine subjects were entered and because all failed to achieve negative urea breath test, the stopping rule required the study to end.

Conclusion

We were unable to confirm reports of achieving a high H. pylori cure rate with L. delbrueckii GLB44 plus a PPI.     

Physiologically Realistic and Validated Mathematical Liver Model Revels Hepatobiliary Transfer Rates for Gd-EOB-DTPA Using Human DCE-MRI Data

Objectives

Diffuse liver disease (DLD), such as non-alcoholic fatty liver disease (NASH) and cirrhosis, is a rapidly growing problem throughout the Westernized world. Magnetic resonance imaging (MRI), based on uptake of the hepatocyte-specific contrast agent (CA) Gd-EOB-DTPA, is a promising non-invasive approach for diagnosing DLD. However, to fully utilize the potential of such dynamic measurements for clinical or research purposes, more advanced methods for data analysis are required.

Methods

A mathematical model that can be used for such data-analysis was developed. Data was obtained from healthy human subjects using a clinical protocol with high spatial resolution. The model is based on ordinary differential equations and goes beyond local diffusion modeling, taking into account the complete system accessible to the CA.

Results

The presented model can describe the data accurately, which was confirmed using chi-square statistics. Furthermore, the model is minimal and identifiable, meaning that all parameters were determined with small degree of uncertainty. The model was also validated using independent data.

Conclusions

We have developed a novel approach for determining previously undescribed physiological hepatic parameters in humans, associated with CA transport across the liver. The method has a potential for assessing regional liver function in clinical examinations of patients that are suffering of DLD and compromised hepatic function.     

Computing expectation values for RNA motifs using discrete convolutions

Background  

Computational biologists use Expectation values (E-values) to estimate the number of solutions that can be expected by chance during a database scan. Here we focus on computing Expectation values for RNA motifs defined by single-strand and helix lod-score profiles with variable helix spans. Such E-values cannot be computed assuming a normal score distribution and their estimation previously required lengthy simulations.     

Combining gene expression data from different generations of oligonucleotide arrays

Background  

One of the important challenges in microarray analysis is to take full advantage of previously accumulated data, both from one's own laboratory and from public repositories. Through a comparative analysis on a variety of datasets, a more comprehensive view of the underlying mechanism or structure can be obtained. However, as we discover in this work, continual changes in genomic sequence annotations and probe design criteria make it difficult to compare gene expression data even from different generations of the same microarray platform.     

A study of polymerase chain reaction device control via cloud using Firebase Cloud Messaging protocol

Background

In this paper, we propose a system for data monitoring and control of polymerase chain reaction (PCR) externally. PCR is a technique for amplifying a desired DNA molecule by repeatedly synthesizing a specific part of DNA sequence. Currently, commercially available systems are standalone systems or operate PCR devices through a computer in the vicinity of devices for control purposes. These systems are limited in the number of devices that the host system can monitor at the same time, and there are limitations in controlling devices or accessing experimental data externally. Therefore, we propose a system to control the PCR device via the cloud for the convenience of the user and to overcome the limitation of the place.

Methods

The cloud system used in this study is Google’s Firebase. At this time, we use Firebase Cloud Messaging (FCM) protocol to send and receive data. In this paper, we have experimented on the possibility of data transmission and reception using FCM between device, cloud and user. Since the PCR chips used in the research are generally operated at about 10°/s, and the temperature can be controlled within 0.5°, the processing period of the control process should be made much smaller than 1/20 s (50 ms).

Results

As a result of experiments, the time of the data round-trip using FCM was measured at 150 ms on the average. Therefore, the data exchange time using FCM is three times slower than the reference time of 50 ms.

Conclusions

Since the data round-trip time using FCM is measured to be three times slower than the reference time of 50 ms, it is impossible for the user to control the device such as the PCR device used in this study through the cloud. However, it is possible for the user to monitor the status of the PCR device from the outside in real time.

     

Misty Mountain clustering: application to fast unsupervised flow cytometry gating

Background  

There are many important clustering questions in computational biology for which no satisfactory method exists. Automated clustering algorithms, when applied to large, multidimensional datasets, such as flow cytometry data, prove unsatisfactory in terms of speed, problems with local minima or cluster shape bias. Model-based approaches are restricted by the assumptions of the fitting functions. Furthermore, model based clustering requires serial clustering for all cluster numbers within a user defined interval. The final cluster number is then selected by various criteria. These supervised serial clustering methods are time consuming and frequently different criteria result in different optimal cluster numbers. Various unsupervised heuristic approaches that have been developed such as affinity propagation are too expensive to be applied to datasets on the order of 10⁶ points that are often generated by high throughput experiments.     

Next generation genome sequencing reveals phylogenetic clades with different level of virulence among Salmonella Typhimurium clinical human isolates in Hong Kong

Background

Salmonella Typhimurium is frequently isolated from foodborne infection cases in Hong Kong, but the lack of genome sequences has hindered in-depth epidemiological and phylogenetic studies. In this study, we sought to reconstruct the phylogenetic relationship and investigate the distribution and mutation patterns of virulence determinants among local S. Typhimurium clinical isolates using their genome sequences.

Results

We obtained genome sequences of 20 S. Typhimurium clinical isolates from a local hospital cluster using a 454 GS FLX Titanium sequencing platform. Phylogenetic analysis was performed based on single nucleotide polymorphism positions of the core genome against the reference strain LT2. Antimicrobial susceptibility was determined using minimal inhibitory concentration for five antimicrobial agents and analyses of virulence determinants were performed through referencing to various databases. Through phylogenetic analysis, we revealed two distinct clades of S. Typhimurium isolates and three outliers in Hong Kong, which differ remarkably in antimicrobial susceptibility and presentation and mutations of virulence determinants. The local isolates were not closely related to many of the previously sequenced S. Typhimurium isolates, except LT2. As the isolates in the two clades spanned over 10 years of isolation, they probably represent endemic strains. The outliers are possibly introduced from outside of Hong Kong. The close relatedness of members in one of the clades to LT2 and the Japanese stool isolate T000240 suggests the potential reemergence of LT2 progeny in regions nearby.

Conclusions

Our study demonstrated the utility of next-generation sequencing coupled to traditional microbiological testing method in a retrospective epidemiological study involving multiple clinical isolates. The evolution of multidrug- and ciprofloxacin-resistant strains among the more virulent clade is also an increasing concern.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1900-y) contains supplementary material, which is available to authorized users.     

High-throughput kinase assays with protein substrates using fluorescent polymer superquenching

Background  

High-throughput screening is used by the pharmaceutical industry for identifying lead compounds that interact with targets of pharmacological interest. Because of the key role that aberrant regulation of protein phosphorylation plays in diseases such as cancer, diabetes and hypertension, kinases have become one of the main drug targets. With the exception of antibody-based assays, methods to screen for specific kinase activity are generally restricted to the use of small synthetic peptides as substrates. However, the use of natural protein substrates has the advantage that potential inhibitors can be detected that affect enzyme activity by binding to a site other than the catalytic site. We have previously reported a non-radioactive and non-antibody-based fluorescence quench assay for detection of phosphorylation or dephosphorylation using synthetic peptide substrates. The aim of this work is to develop an assay for detection of phosphorylation of chemically unmodified proteins based on this polymer superquenching platform.     

Identification and correction of previously unreported spatial phenomena using raw Illumina BeadArray data

Background  

A key stage for all microarray analyses is the extraction of feature-intensities from an image. If this step goes wrong, then subsequent preprocessing and processing stages will stand little chance of rectifying the matter. Illumina employ random construction of their BeadArrays, making feature-intensity extraction even more important for the Illumina platform than for other technologies. In this paper we show that using raw Illumina data it is possible to identify, control, and perhaps correct for a range of spatial-related phenomena that affect feature-intensity extraction.     

Validating viral quasispecies with digital organisms: a re-examination of the critical mutation rate

Background  

In this report we re-examine some recent experiments with digital organisms to test some predictions of quasispecies theory. These experiments revealed that under high mutation rates populations of less fit organisms previously adapted to such high mutation rates were able to outcompete organisms with higher average fitness but adapted to low mutation rates.     

BASE - 2nd generation software for microarray data management and analysis

Background  

Microarray experiments are increasing in size and samples are collected asynchronously over long time. Available data are re-analysed as more samples are hybridized. Systematic use of collected data requires tracking of biomaterials, array information, raw data, and assembly of annotations. To meet the information tracking and data analysis challenges in microarray experiments we reimplemented and improved BASE version 1.2.     

First Things First: Effectiveness and Scalability of a Basic Prehospital Trauma Care Program for Lay First-Responders in Kampala,Uganda

Background

We previously showed that in the absence of a formal emergency system, lay people face a heavy burden of injuries in Kampala, Uganda, and we demonstrated the feasibility of a basic prehospital trauma course for lay people. This study tests the effectiveness of this course and estimates the costs and cost-effectiveness of scaling up this training.

Methods and Findings

For six months, we prospectively followed 307 trainees (police, taxi drivers, and community leaders) who completed a one-day basic prehospital trauma care program in 2008. Cross-sectional surveys and fund of knowledge tests were used to measure their frequency of skill and supply use, reasons for not providing aid, perceived utility of the course and kit, confidence in using skills, and knowledge of first-aid. We then estimated the cost-effectiveness of scaling up the program.At six months, 188 (62%) of the trainees were followed up. Their knowledge retention remained high or increased. The mean correct score on a basic fund of knowledge test was 92%, up from 86% after initial training (n = 146 pairs, p = 0.0016). 97% of participants had used at least one skill from the course: most commonly haemorrhage control, recovery position and lifting/moving and 96% had used at least one first-aid item. Lack of knowledge was less of a barrier and trainees were significantly more confident in providing first-aid. Based on cost estimates from the World Health Organization, local injury data, and modelling from previous studies, the projected cost of scaling up this program was $0.12 per capita or $25–75 per life year saved. Key limitations of the study include small sample size, possible reporter bias, preliminary local validation of study instruments, and an indirect estimate of mortality reduction.

Conclusions

Lay first-responders effectively retained knowledge on prehospital trauma care and confidently used their first-aid skills and supplies for at least six months. The costs of scaling up this intervention to cover Kampala are very modest. This may be a cost-effective first step toward developing formal emergency services in Uganda other resource-constrained settings. Further research is needed in this critical area of trauma care in low-income countries.     

New resampling method for evaluating stability of clusters

Background  

Hierarchical clustering is a widely applied tool in the analysis of microarray gene expression data. The assessment of cluster stability is a major challenge in clustering procedures. Statistical methods are required to distinguish between real and random clusters. Several methods for assessing cluster stability have been published, including resampling methods such as the bootstrap.     

Iterative refinement of structure-based sequence alignments by Seed Extension

Background  

Accurate sequence alignment is required in many bioinformatics applications but, when sequence similarity is low, it is difficult to obtain accurate alignments based on sequence similarity alone. The accuracy improves when the structures are available, but current structure-based sequence alignment procedures still mis-align substantial numbers of residues. In order to correct such errors, we previously explored the possibility of replacing the residue-based dynamic programming algorithm in structure alignment procedures with the Seed Extension algorithm, which does not use a gap penalty. Here, we describe a new procedure called RSE (Refinement with Seed Extension) that iteratively refines a structure-based sequence alignment.