GeneBrowser 2: an application to explore and identify common biological traits in a set of genes

Background  

The development of high-throughput laboratory techniques created a demand for computer-assisted result analysis tools. Many of these techniques return lists of genes whose interpretation requires finding relevant biological roles for the problem at hand. The required information is typically available in public databases, and usually, this information must be manually retrieved to complement the analysis. This process is a very time-consuming task that should be automated as much as possible.     

Motmot, an open-source toolkit for realtime video acquisition and analysis

Background  

Video cameras sense passively from a distance, offer a rich information stream, and provide intuitively meaningful raw data. Camera-based imaging has thus proven critical for many advances in neuroscience and biology, with applications ranging from cellular imaging of fluorescent dyes to tracking of whole-animal behavior at ecologically relevant spatial scales.     

Inferring functional modules of protein families with probabilistic topic models

Background  

Genome and metagenome studies have identified thousands of protein families whose functions are poorly understood and for which techniques for functional characterization provide only partial information. For such proteins, the genome context can give further information about their functional context.     

Electrical impedance tomography imaging using a priori ultrasound data

Background  

Different imaging systems (e.g. electrical, magnetic, and ultrasound) rely on a wide variety of physical properties, and the datasets obtained from such systems provide only partial information about the unknown true state. One approach is to choose complementary imaging systems, and to combine the information to achieve a better representation.     

New components of the <Emphasis Type="Italic">Dictyostelium</Emphasis> PKA pathway revealed by Bayesian analysis of expression data

Background  

Identifying candidate genes in genetic networks is important for understanding regulation and biological function. Large gene expression datasets contain relevant information about genetic networks, but mining the data is not a trivial task. Algorithms that infer Bayesian networks from expression data are powerful tools for learning complex genetic networks, since they can incorporate prior knowledge and uncover higher-order dependencies among genes. However, these algorithms are computationally demanding, so novel techniques that allow targeted exploration for discovering new members of known pathways are essential.     

Assessing protein similarity with Gene Ontology and its use in subnuclear localization prediction

Background  

The accomplishment of the various genome sequencing projects resulted in accumulation of massive amount of gene sequence information. This calls for a large-scale computational method for predicting protein localization from sequence. The protein localization can provide valuable information about its molecular function, as well as the biological pathway in which it participates. The prediction of localization of a protein at subnuclear level is a challenging task. In our previous work we proposed an SVM-based system using protein sequence information for this prediction task. In this work, we assess protein similarity with Gene Ontology (GO) and then improve the performance of the system by adding a module of nearest neighbor classifier using a similarity measure derived from the GO annotation terms for protein sequences.     

Edge detection in microscopy images using curvelets

Background  

Despite significant progress in imaging technologies, the efficient detection of edges and elongated features in images of intracellular and multicellular structures acquired using light or electron microscopy is a challenging and time consuming task in many laboratories.     

Non-rigid registration of a 3D ultrasound and a MR image data set of the female pelvic floor using a biomechanical model

Background  

The visual combination of different modalities is essential for many medical imaging applications in the field of Computer-Assisted medical Diagnosis (CAD) to enhance the clinical information content. Clinically, incontinence is a diagnosis with high clinical prevalence and morbidity rate. The search for a method to identify risk patients and to control the success of operations is still a challenging task. The conjunction of magnetic resonance (MR) and 3D ultrasound (US) image data sets could lead to a new clinical visual representation of the morphology as we show with corresponding data sets of the female anal canal with this paper.     

Shortest triplet clustering: reconstructing large phylogenies using representative sets

Background  

Understanding the evolutionary relationships among species based on their genetic information is one of the primary objectives in phylogenetic analysis. Reconstructing phylogenies for large data sets is still a challenging task in Bioinformatics.     

Tools for loading MEDLINE into a local relational database

Background  

Researchers who use MEDLINE for text mining, information extraction, or natural language processing may benefit from having a copy of MEDLINE that they can manage locally. The National Library of Medicine (NLM) distributes MEDLINE in eXtensible Markup Language (XML)-formatted text files, but it is difficult to query MEDLINE in that format. We have developed software tools to parse the MEDLINE data files and load their contents into a relational database. Although the task is conceptually straightforward, the size and scope of MEDLINE make the task nontrivial. Given the increasing importance of text analysis in biology and medicine, we believe a local installation of MEDLINE will provide helpful computing infrastructure for researchers.     

Description of a PCR-based technique for DNA splicing and mutagenesis by producing 5' overhangs with run through stop DNA synthesis utilizing Ara-C

Background  

Splicing of DNA molecules is an important task in molecular biology that facilitates cloning, mutagenesis and creation of chimeric genes. Mutagenesis and DNA splicing techniques exist, some requiring restriction enzymes, and others utilize staggered reannealing approaches.     

Simultaneous fMRI and EEG during the Multi-Source Interference Task

Background

fMRI and EEG are two non-invasive functional imaging techniques within cognitive neuroscience that have complementary advantages to obtain both temporal and spatial information. The multi-source interference task (MSIT) has been shown to generate robust activations of the dorsal anterior cingulate cortex (dACC) on both a single-subject level and in group averages, in fMRI studies. We have now simultaneously acquired fMRI and EEG during a cognitive interference task.

Materials and Methods

Healthy volunteers were tested in an MRI scanner with simultaneous EEG and fMRI recordings during the MSIT.

Results

The interference condition significantly increased the reaction time in the task. The fMRI analyses revealed activation of dACC as expected, in all subjects at the individual level and in group analyses. The posterior cingulate cortex was de-activated. Simultaneous EEG showed the expected anterior distribution of the interference effect, as it was restricted to frontal sites within a time frame of 80–120 ms post response.

Conclusion

The MSIT task is a reliable task for interference evaluation. fMRI shows robust activation of dACC and by adding EEG, an interference effect can be noticed within a temporal interval of 80–120 ms after the response, as a CRN (correct response negativity). This means that EEG could add a more detailed temporal aspect to the fMRI data from an interference task, and that despite the hostile environment within an MRI scanner, EEG data could be used.     

A bi-ordering approach to linking gene expression with clinical annotations in gastric cancer

Background  

In the study of cancer genomics, gene expression microarrays, which measure thousands of genes in a single assay, provide abundant information for the investigation of interesting genes or biological pathways. However, in order to analyze the large number of noisy measurements in microarrays, effective and efficient bioinformatics techniques are needed to identify the associations between genes and relevant phenotypes. Moreover, systematic tests are needed to validate the statistical and biological significance of those discoveries.     

Priming nonlinear searches for pathway identification

Background  

Dense time series of metabolite concentrations or of the expression patterns of proteins may be available in the near future as a result of the rapid development of novel, high-throughput experimental techniques. Such time series implicitly contain valuable information about the connectivity and regulatory structure of the underlying metabolic or proteomic networks. The extraction of this information is a challenging task because it usually requires nonlinear estimation methods that involve iterative search algorithms. Priming these algorithms with high-quality initial guesses can greatly accelerate the search process. In this article, we propose to obtain such guesses by preprocessing the temporal profile data and fitting them preliminarily by multivariate linear regression.     

Predicting cancer involvement of genes from heterogeneous data

Background  

Systematic approaches for identifying proteins involved in different types of cancer are needed. Experimental techniques such as microarrays are being used to characterize cancer, but validating their results can be a laborious task. Computational approaches are used to prioritize between genes putatively involved in cancer, usually based on further analyzing experimental data.     

Parallel biocomputing

Background  

With the advent of high throughput genomics and high-resolution imaging techniques, there is a growing necessity in biology and medicine for parallel computing, and with the low cost of computing, it is now cost-effective for even small labs or individuals to build their own personal computation cluster.     

Exploring supervised and unsupervised methods to detect topics in biomedical text

Background  

Topic detection is a task that automatically identifies topics (e.g., "biochemistry" and "protein structure") in scientific articles based on information content. Topic detection will benefit many other natural language processing tasks including information retrieval, text summarization and question answering; and is a necessary step towards the building of an information system that provides an efficient way for biologists to seek information from an ocean of literature.     

Response Inhibition Impairment in High Functioning Autism and Attention Deficit Hyperactivity Disorder: Evidence from Near-Infrared Spectroscopy Data

Background

Response inhibition, an important domain of executive function (EF), involves the ability to suppress irrelevant or interfering information and impulses. Previous studies have shown impairment of response inhibition in high functioning autism (HFA) and attention deficit hyperactivity disorder (ADHD), but more recent findings have been inconsistent. To date, almost no studies have been conducted using functional imaging techniques to directly compare inhibitory control between children with HFA and those with ADHD.

Method

Nineteen children with HFA, 16 age- and intelligence quotient (IQ)-matched children with ADHD, and 16 typically developing (TD) children were imaged using functional near-infrared spectroscopy (NIRS) while performing Go/No-go and Stroop tasks.

Results

Compared with the TD group, children in both the HFA and ADHD groups took more time to respond during the No-go blocks, with reaction time longest for HFA and shortest for TD. Children in the HFA and ADHD groups also made a greater number of reaction errors in the No-go blocks than those in the TD group. During the Stroop task, there were no significant differences between these three groups in reaction time and omission errors. Both the HFA and ADHD groups showed a higher level of inactivation in the right prefrontal cortex (PFC) during the No-go blocks, relative to the TD group. However, no significant differences were found between groups in the levels of oxyhemoglobin concentration in the PFC during the Stroop task.

Conclusion

Functional brain imaging using NIRS showed reduced activation in the right PFC in children with HFA or ADHD during an inhibition task, indicating that inhibitory dysfunction is a shared feature of both HFA and ADHD.     

Hepatitis C virus whole genome position weight matrix and robust primer design

Background  

The high degree of sequence heterogeneity found in Hepatitis C virus (HCV) isolates, makes robust nucleic acid-based assays difficult to generate. Polymerase chain reaction based techniques, require efficient and specific sequence recognition. Generation of robust primers capable of recognizing a wide range of isolates is a difficult task.     

A new approach to dual-color two-photon microscopy with fluorescent proteins

Background  

Two-photon dual-color imaging of tissues and cells labeled with fluorescent proteins (FPs) is challenging because most two-photon microscopes only provide one laser excitation wavelength at a time. At present, methods for two-photon dual-color imaging are limited due to the requirement of large differences in Stokes shifts between the FPs used and their low two-photon absorption (2PA) efficiency.