Bioinformatics Challenges Research Articles

Large Scale Comparative Visualisation of Regulatory Networks with TRNDiff

The advent of Next Generation Sequencing (NGS) technologies has seen explosive growth in genomic datasets, and dense coverage of related organisms, supporting study of subtle, strain-specific variations as a determinant of function. Such data collections present fresh and complex challenges for bioinformatics, those of comparing models of complex relationships across hundreds and even thousands of sequences. Transcriptional Regulatory Network (TRN) structures document the influence of regulatory proteins called Transcription Factors (TFs) on associated Target Genes (TGs). TRNs are routinely inferred from model systems or iterative search, and analysis at these scales requires simultaneous displays of multiple networks well beyond those of existing network visualisation tools [1]. In this paper we describe TRNDiff, an open source system supporting the comparative analysis and visualization of TRNs (and similarly structured data) from many genomes, allowing rapid identification of functional variations within species. The approach is demonstrated through a small scale multiple TRN analysis of the Fur iron-uptake system of Yersinia, suggesting a number of candidate virulence factors; and through a larger study exploiting integration with the RegPrecise database (http://regprecise.lbl.gov; [2]) - a collection of hundreds of manually curated and predicted transcription factor regulons drawn from across the entire spectrum of prokaryotic organisms.

8 th Workshop on Biomedical and Bioinformatics Challenges for Computer Science – BBC2015

Agapito, G., Cannataro, M., Castelli, M., Dondi, R., & Zoppis, I. (2017). 10th Workshop on Biomedical and Bioinformatics Challenges for Computer Science - BBC2017. Procedia Computer Science, 108, 1113-1114. https://doi.org/10.1016/j.procs.2017.05.279

Bioinformatics approaches, challenges and performance of diversity profiling on three next generation sequencing platforms

Bioinformatics approaches, challenges and performance of diversity profiling on three next generation sequencing platforms

Achievements and challenges in structural bioinformatics and computational biophysics.

Motivation: The field of structural bioinformatics and computational biophysics has undergone a revolution in the last 10 years. Developments that are captured annually through the 3DSIG meeting, upon which this article reflects.Results: An increase in the accessible data, computational resources and methodology has resulted in an increase in the size and resolution of studied systems and the complexity of the questions amenable to research. Concomitantly, the parameterization and efficiency of the methods have markedly improved along with their cross-validation with other computational and experimental results.Conclusion: The field exhibits an ever-increasing integration with biochemistry, biophysics and other disciplines. In this article, we discuss recent achievements along with current challenges within the field.Contact: Rafael.Najmanovich@USherbrooke.ca

Finding gene regulatory network candidates using the gene expression knowledge base.

BackgroundNetwork-based approaches for the analysis of large-scale genomics data have become well established. Biological networks provide a knowledge scaffold against which the patterns and dynamics of ‘omics’ data can be interpreted. The background information required for the construction of such networks is often dispersed across a multitude of knowledge bases in a variety of formats. The seamless integration of this information is one of the main challenges in bioinformatics. The Semantic Web offers powerful technologies for the assembly of integrated knowledge bases that are computationally comprehensible, thereby providing a potentially powerful resource for constructing biological networks and network-based analysis.ResultsWe have developed the Gene eXpression Knowledge Base (GeXKB), a semantic web technology based resource that contains integrated knowledge about gene expression regulation. To affirm the utility of GeXKB we demonstrate how this resource can be exploited for the identification of candidate regulatory network proteins. We present four use cases that were designed from a biological perspective in order to find candidate members relevant for the gastrin hormone signaling network model. We show how a combination of specific query definitions and additional selection criteria derived from gene expression data and prior knowledge concerning candidate proteins can be used to retrieve a set of proteins that constitute valid candidates for regulatory network extensions.ConclusionsSemantic web technologies provide the means for processing and integrating various heterogeneous information sources. The GeXKB offers biologists such an integrated knowledge resource, allowing them to address complex biological questions pertaining to gene expression. This work illustrates how GeXKB can be used in combination with gene expression results and literature information to identify new potential candidates that may be considered for extending a gene regulatory network.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-014-0386-y) contains supplementary material, which is available to authorized users.

G-Links: a gene-centric link acquisition service

With the availability of numerous curated databases, researchers are now able to efficiently use the multitude of biological data by integrating these resources via hyperlinks and cross-references. A large proportion of bioinformatics research tasks, however, may include labor-intensive tasks such as fetching, parsing, and merging datasets and functional annotations from distributed multi-domain databases. This data integration issue is one of the key challenges in bioinformatics. We aim to solve this problem with a service named G-Links, 1) by gathering resource URI information from 130 databases and 30 web services in a gene-centric manner so that users can retrieve all available links about a given gene, 2) by providing RESTful API for easy retrieval of links including facet searching based on keywords and/or predicate types, and 3) by producing a variety of outputs as visual HTML page, tab-delimited text, and in Semantic Web formats such as Notation3 and RDF. G-Links as well as other relevant documentation are available at http://link.g-language.org/

Reproducible simulations of realistic samples for next-generation sequencing studies using Variant Simulation Tools.

Computer simulations have been widely used to validate and evaluate the power of statistical methods for genetic epidemiological studies. Although a large number of simulation methods and software packages have been developed for genome-wide association studies, methodological and bioinformatics challenges have limited their applications in simulating datasets for whole-genome and whole-exome sequencing studies. With the development of more sophisticated statistical methods that make fuller use of available data and our knowledge of the human genome, there is a pressing need for genetic simulators that capture more features of empirical data (e.g., multiallele variants, indels, use of the Variant Call Format) and the human genome (e.g., functional annotations of genetic variants). This article introduces Variant Simulation Tools (VST), a module of Variant Tools for the simulation of genetic variants for sequencing-based genetic epidemiological studies. Although multiple simulation engines are provided, the core of VST is a novel forward-time simulation engine that simulates real nucleotide sequences of the human genome using DNA mutation models, fine-scale recombination maps, and a selection model based on amino acid changes of translated protein sequences. The design of VST allows users to easily create and distribute simulation methods and simulated datasets for a variety of applications and encourages fair comparison between statistical methods through the use of existing or reproduced simulated datasets.

Measuring epigenetics as the mediator of gene/environment interactions in DOHaD.

Analysis of DNA methylation data in epigenome-wide association studies provides many bioinformatics and statistical challenges. Not least of these, are the non-independence of individual DNA methylation marks from each other, from genotype and from technical sources of variation. In this review we discuss DNA methylation data from the Infinium450K array and processing methodologies to reduce technical variation. We describe recent approaches to harness the concordance of neighbouring DNA methylation values to improve power in association studies. We also describe how the non-independence of genotype and DNA methylation has been used to infer causality (in the case of Mendelian randomization approaches); suggest the mediating effect of DNA methylation in linking intergenic single nucleotide polymorphisms, identified in genome-wide association studies, to phenotype; and to uncover the widespread influence of gene and environment interactions on methylation levels.

The Potential of Genome-Wide Analyses to Improve Non-Small-Cell Lung Cancer Care

SUMMARY Genomic technologies have revolutionized the way we study and understand cancer. The advent of next-generation sequencing technology in particular is now starting to change the clinical management of non-small-cell lung cancer. These technologies have helped us to refine prognostication and identify new driver mutations that can allow subselection of patients for therapeutic intervention. However, several limitations and challenges must be overcome before these technologies are widely accepted in diagnostic laboratories. It will be important for clinicians and diagnostic laboratories to consider sample type, analytical platform, cost, data security and ethics, and the bioinformatics challenges associated with 'big data', before widespread integration to the clinic. If these challenges can be overcome, then genomics has the potential to change clinical management of lung cancer.

A simple extension to the CMASA method for the prediction of catalytic residues in the presence of single point mutations.

The automatic identification of catalytic residues still remains an important challenge in structural bioinformatics. Sequence-based methods are good alternatives when the query shares a high percentage of identity with a well-annotated enzyme. However, when the homology is not apparent, which occurs with many structures from the structural genome initiative, structural information should be exploited. A local structural comparison is preferred to a global structural comparison when predicting functional residues. CMASA is a recently proposed method for predicting catalytic residues based on a local structure comparison. The method achieves high accuracy and a high value for the Matthews correlation coefficient. However, point substitutions or a lack of relevant data strongly affect the performance of the method. In the present study, we propose a simple extension to the CMASA method to overcome this difficulty. Extensive computational experiments are shown as proof of concept instances, as well as for a few real cases. The results show that the extension performs well when the catalytic site contains mutated residues or when some residues are missing. The proposed modification could correctly predict the catalytic residues of a mutant thymidylate synthase, 1EVF. It also successfully predicted the catalytic residues for 3HRC despite the lack of information for a relevant side chain atom in the PDB file.

A Bayesian Framework to Identify Methylcytosines from High-Throughput Bisulfite Sequencing Data

High-throughput bisulfite sequencing technologies have provided a comprehensive and well-fitted way to investigate DNA methylation at single-base resolution. However, there are substantial bioinformatic challenges to distinguish precisely methylcytosines from unconverted cytosines based on bisulfite sequencing data. The challenges arise, at least in part, from cell heterozygosis caused by multicellular sequencing and the still limited number of statistical methods that are available for methylcytosine calling based on bisulfite sequencing data. Here, we present an algorithm, termed Bycom, a new Bayesian model that can perform methylcytosine calling with high accuracy. Bycom considers cell heterozygosis along with sequencing errors and bisulfite conversion efficiency to improve calling accuracy. Bycom performance was compared with the performance of Lister, the method most widely used to identify methylcytosines from bisulfite sequencing data. The results showed that the performance of Bycom was better than that of Lister for data with high methylation levels. Bycom also showed higher sensitivity and specificity for low methylation level samples (<1%) than Lister. A validation experiment based on reduced representation bisulfite sequencing data suggested that Bycom had a false positive rate of about 4% while maintaining an accuracy of close to 94%. This study demonstrated that Bycom had a low false calling rate at any methylation level and accurate methylcytosine calling at high methylation levels. Bycom will contribute significantly to studies aimed at recalibrating the methylation level of genomic regions based on the presence of methylcytosines.

Transcriptome-guided amyloid imaging genetic analysis via a novel structured sparse learning algorithm.

Motivation: Imaging genetics is an emerging field that studies the influence of genetic variation on brain structure and function. The major task is to examine the association between genetic markers such as single-nucleotide polymorphisms (SNPs) and quantitative traits (QTs) extracted from neuroimaging data. The complexity of these datasets has presented critical bioinformatics challenges that require new enabling tools. Sparse canonical correlation analysis (SCCA) is a bi-multivariate technique used in imaging genetics to identify complex multi-SNP–multi-QT associations. However, most of the existing SCCA algorithms are designed using the soft thresholding method, which assumes that the input features are independent from one another. This assumption clearly does not hold for the imaging genetic data. In this article, we propose a new knowledge-guided SCCA algorithm (KG-SCCA) to overcome this limitation as well as improve learning results by incorporating valuable prior knowledge.Results: The proposed KG-SCCA method is able to model two types of prior knowledge: one as a group structure (e.g. linkage disequilibrium blocks among SNPs) and the other as a network structure (e.g. gene co-expression network among brain regions). The new model incorporates these prior structures by introducing new regularization terms to encourage weight similarity between grouped or connected features. A new algorithm is designed to solve the KG-SCCA model without imposing the independence constraint on the input features. We demonstrate the effectiveness of our algorithm with both synthetic and real data. For real data, using an Alzheimer’s disease (AD) cohort, we examine the imaging genetic associations between all SNPs in the APOE gene (i.e. top AD gene) and amyloid deposition measures among cortical regions (i.e. a major AD hallmark). In comparison with a widely used SCCA implementation, our KG-SCCA algorithm produces not only improved cross-validation performances but also biologically meaningful results.Availability: Software is freely available on request.Contact: shenli@iu.edu

Rare Disease Research Roadmap: Navigating the bioinformatics and translational challenges for improved patient health outcomes

Rare disease registries have now been recognized as a global priority for progress both in monitoring and documenting the natural course, and preventing and treating rare diseases. However, a disease registry is only one element of rare disease translational research. Here, we outline what we believe are ten key components in comprehensive rare disease translational research and describe critical relationships between them. These components are: (i) client–practitioner partnerships; (ii) disease registries; (iii) biobanks; (iv) genomics and other -omics platforms; (v) community-based and population-wide studies; (vi) bioinformatics and high performance computing; (vii) interactions with pharma to facilitate drug discovery; (viii) personalized treatments based on genotype-phenotype correlations; (ix) eHealth and a whole of life record; and (x) regulatory frameworks, particularly with regard to specimen and data sharing, and the return of results. Each component has its own inherent complexity, but if effectively integrated they will provide a comprehensive approach to the future management of rare diseases, and aid health care providers in delivering services to individuals affected with rare diseases. We demonstrate that navigation through the roadmap can provide relevant health stakeholders with a blueprint to understand the challenges and barriers which need to be overcome within and across the constituent components. The rare disease roadmap will assist decision-making at all health stakeholder levels and enable the seamless integration of new knowledge, standard operating procedures and the implementation of best practice.

MEGADOCK 4.0: an ultra-high-performance protein-protein docking software for heterogeneous supercomputers.

Summary: The application of protein–protein docking in large-scale interactome analysis is a major challenge in structural bioinformatics and requires huge computing resources. In this work, we present MEGADOCK 4.0, an FFT-based docking software that makes extensive use of recent heterogeneous supercomputers and shows powerful, scalable performance of >97% strong scaling.Availability and Implementation: MEGADOCK 4.0 is written in C++ with OpenMPI and NVIDIA CUDA 5.0 (or later) and is freely available to all academic and non-profit users at: http://www.bi.cs.titech.ac.jp/megadock.Contact: akiyama@cs.titech.ac.jpSupplementary information: Supplementary data are available at Bioinformatics online

IN-MACA-MCC: Integrated Multiple Attractor Cellular Automata with Modified Clonal Classifier for Human Protein Coding and Promoter Prediction.

Protein coding and promoter region predictions are very important challenges of bioinformatics (Attwood and Teresa, 2000). The identification of these regions plays a crucial role in understanding the genes. Many novel computational and mathematical methods are introduced as well as existing methods that are getting refined for predicting both of the regions separately; still there is a scope for improvement. We propose a classifier that is built with MACA (multiple attractor cellular automata) and MCC (modified clonal classifier) to predict both regions with a single classifier. The proposed classifier is trained and tested with Fickett and Tung (1992) datasets for protein coding region prediction for DNA sequences of lengths 54, 108, and 162. This classifier is trained and tested with MMCRI datasets for protein coding region prediction for DNA sequences of lengths 252 and 354. The proposed classifier is trained and tested with promoter sequences from DBTSS (Yamashita et al., 2006) dataset and nonpromoters from EID (Saxonov et al., 2000) and UTRdb (Pesole et al., 2002) datasets. The proposed model can predict both regions with an average accuracy of 90.5% for promoter and 89.6% for protein coding region predictions. The specificity and sensitivity values of promoter and protein coding region predictions are 0.89 and 0.92, respectively.

Integrative and interdisciplinary challenges in translational bioinformatics

Translational bioinformatics (TBI) is an emerging interdisciplinary field, which aims to bridge the gap between molecular world and clinical world. Translational bioinformatics employs data mining and machine learning techniques to analyze increasingly massive biomedical data and generate knowledge for clinical applications. One of the major challenges in TBI is to integrate multi-dimensional heterogeneous biomedical information sources in order to elucidate new biomedical knowledge. The integrative methodologies that are used to interpret these data require expertise in different disciplines, such as biology, medicine, mathematics, statistics and bioinformatics, and they pose great interdisciplinary challenges. Bioinformatics, system biology and network science together with knowledge engineering and reverse engineering have great potential to push TBI forward. In this paper, we introduce the background of TBI and the great variety of biomedical data, discuss the computational tools for integrative analyses, and summarize several crucial interdisciplinary challenges and future directions in TBI.

The intriguing complexities of mammalian gene regulation: how to link enhancers to regulated genes. Are we there yet?

The information encoded in genomes supports the differentiation and function of the more than 200 unique cell types, which exist in various mammalian species. The major mechanism driving cellular differentiation and specification is differential gene expression regulation. Cis-acting enhancers and silencers appear to have key roles in regulating the expression of mammalian genes. However, these cis-acting elements are often located very far away from the regulated gene. Therefore, it is hard to find all of them and link them to the regulated gene. An intriguing and unresolved issue of the field is to identify all of the enhancers of a particular gene and link these short regulatory sequences to the genes they regulate and thus, reliably identify gene regulatory enhancer networks. Recent advances in molecular biological methods coupled with Next-Generation Sequencing (NGS) technologies have opened up new possibilities in this area of genomics. In this review we summarize the technological advances, bioinformatics challenges and the potential molecular mechanisms allowing the construction of enhancer networks operating in specific cell types and/or activated by various signals.

A cloud-compatible bioinformatics pipeline for ultrarapid pathogen identification from next-generation sequencing of clinical samples

Unbiased next-generation sequencing (NGS) approaches enable comprehensive pathogen detection in the clinical microbiology laboratory and have numerous applications for public health surveillance, outbreak investigation, and the diagnosis of infectious diseases. However, practical deployment of the technology is hindered by the bioinformatics challenge of analyzing results accurately and in a clinically relevant timeframe. Here we describe SURPI (“sequence-based ultrarapid pathogen identification”), a computational pipeline for pathogen identification from complex metagenomic NGS data generated from clinical samples, and demonstrate use of the pipeline in the analysis of 237 clinical samples comprising more than 1.1 billion sequences. Deployable on both cloud-based and standalone servers, SURPI leverages two state-of-the-art aligners for accelerated analyses, SNAP and RAPSearch, which are as accurate as existing bioinformatics tools but orders of magnitude faster in performance. In fast mode, SURPI detects viruses and bacteria by scanning data sets of 7–500 million reads in 11 min to 5 h, while in comprehensive mode, all known microorganisms are identified, followed by de novo assembly and protein homology searches for divergent viruses in 50 min to 16 h. SURPI has also directly contributed to real-time microbial diagnosis in acutely ill patients, underscoring its potential key role in the development of unbiased NGS-based clinical assays in infectious diseases that demand rapid turnaround times.

MetDisease—connecting metabolites to diseases via literature

In recent years, metabolomics has emerged as an approach to perform large-scale characterization of small molecules in biological systems. Metabolomics posed a number of bioinformatics challenges associated in data analysis and interpretation. Genome-based metabolic reconstructions have established a powerful framework for connecting metabolites to genes through metabolic reactions and enzymes that catalyze them. Pathway databases and bioinformatics tools that use this framework have proven to be useful for annotating experimental metabolomics data. This framework can be used to infer connections between metabolites and diseases through annotated disease genes. However, only about half of experimentally detected metabolites can be mapped to canonical metabolic pathways. We present a new Cytoscape 3 plug-in, MetDisease, which uses an alternative approach to link metabolites to disease information. MetDisease uses Medical Subject Headings (MeSH) disease terms mapped to PubChem compounds through literature to annotate compound networks. MetDisease can be downloaded from http://apps.cytoscape.org/apps/metdisease or installed via the Cytoscape app manager. Further information about MetDisease can be found at http://metdisease.ncibi.org akarnovs@med.umich.edu Supplementary Data are available at Bioinformatics online.

Multi-perspective quality control of Illumina exome sequencing data using QC3

Advances in next-generation sequencing (NGS) technologies have greatly improved our ability to detect genomic variants for biomedical research. The advance in NGS technologies has also created significant challenges in bioinformatics. One of the major challenges is the quality control of sequencing data. There has been heavy focus on performing raw data quality control. In order to correctly interpret the quality of the DNA sequencing data, however, proper quality control should be conducted at all stages of DNA sequencing data analysis: raw data, alignment, and variant detection. We designed QC3, a quality control tool aimed at those three major stages of DNA sequencing. QC3 monitors quality control metrics at each stage of NGS data and provides unique and independent evaluations of the data quality from different perspectives. QC3 offers unique features such as detection of batch effect and cross contamination. QC3 and its source code are freely downloadable at https://github.com/slzhao/QC3.