Computational Biology Research Research Articles

Data-Driven and Knowledge-Based Algorithms for Gene Network Reconstruction on High-Dimensional Data.

Previous efforts in gene network reconstruction have mainly focused on data-driven modeling, with little attention paid to knowledge-based approaches. Leveraging prior knowledge, however, is a promising paradigm that has been gaining momentum in network reconstruction and computational biology research communities. This paper proposes two new algorithms for reconstructing a gene network from expression profiles with and without prior knowledge in small sample and high-dimensional settings. First, using tools from the statistical estimation theory, particularly the empirical Bayesian approach, the current research estimates a covariance matrix via the shrinkage method. Second, estimated covariance matrix is employed in the penalized normal likelihood method to select the Gaussian graphical model. This formulation allows the application of prior knowledge in the covariance estimation, as well as in the Gaussian graphical model selection. Experimental results on simulated and real datasets show that, compared to state-of-the-art methods, the proposed algorithms achieve better results in terms of both PR and ROC curves. Finally, the present work applies its method on the RNA-seq data of human gastric atrophy patients, which was obtained from the EMBL-EBI database. The source codes and relevant data can be downloaded from: https://github.com/AbbaszadehO/DKGN.

Abstract 3214: BART: An integrative bioinformatics toolkit and web server for functional transcription factor prediction

Abstract The goal of this bioinformatics tool is to predict functional transcription factors (TFs) from various queries of target genomic features. TFs play a key role in regulating gene expression in a dynamic chromatin structure. From a target gene set or a genomic region set, it is challenging to identify functional TFs that regulate these genes or bind in these regions, because of complexity of enhancer distributions and insufficient specificity of DNA sequence motifs. Here, we present BART (Binding Analysis for Regulation of Transcription), an interactive bioinformatics toolkit and web server to predict functional TFs that regulate a set of co-regulated genes or associate with a genomic profile. From a gene set, BART first uses a machine learning approach to generate a genome-wide cis-regulatory profile by leveraging hundreds of publicly available ChIP-seq profiles for active enhancer histone mark H3K27ac and DNase-seq profiles for genome-wide cis-regulatory elements. Then it identifies a list of TFs whose binding profiles most correlate with the cis-regulatory profile by comparing with tens of thousands of ChIP-seq datasets. BART can also identify TFs associated with differential chromatin interaction from a pair of three-dimensional (3D) genomic datasets, such as Hi-C. BART integrates over 12,000 ChIP-seq datasets for over 900 TFs for human and over 500 TFs for mouse to make accurate predictions. We demonstrate that BART outperforms existing methods in identifying the true TF from target gene sets. BART can be useful for any researcher in studying cancer gene regulation and cancer epigenetics, and demonstrates the power of utilizing public data for computational biology research. BART web server is freely available at: http://bartweb.org. Citation Format: Zhenjia Wang, Wenjing Ma, Yifan Zhang, Neal E. Magee, Yang Chen, Chongzhi Zang. BART: An integrative bioinformatics toolkit and web server for functional transcription factor prediction [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3214.

Information Theory in Computational Biology: Where We Stand Today.

“A Mathematical Theory of Communication” was published in 1948 by Claude Shannon to address the problems in the field of data compression and communication over (noisy) communication channels. Since then, the concepts and ideas developed in Shannon’s work have formed the basis of information theory, a cornerstone of statistical learning and inference, and has been playing a key role in disciplines such as physics and thermodynamics, probability and statistics, computational sciences and biological sciences. In this article we review the basic information theory based concepts and describe their key applications in multiple major areas of research in computational biology—gene expression and transcriptomics, alignment-free sequence comparison, sequencing and error correction, genome-wide disease-gene association mapping, metabolic networks and metabolomics, and protein sequence, structure and interaction analysis.

Pr[m]: An Algorithm for Protein Motif Discovery.

Motifs are the evolutionarily conserved patterns which are reported to serve the crucial structural and functional role. Identification of motif patterns in a set of protein sequences has been a prime concern for researchers in computational biology. The discovery of such a protein motif using existing algorithms is purely based on the parameters derived from sequence composition and length. However, the discovery of variable length motif remains a challenging task, as it is not possible to determine the length of a motif in advance. In current work, a k-mer based motif discovery approach called Pr[m], is proposed for the detection of the statistically significant un-gapped motif patterns, with or without wildcard characters. In order to analyze the performance of the proposed approach, a comparative study was performed with MEME and GLAM2, which are two widely used non-discriminative methods for motif discovery. A set of 7,500 test dataset were used to compare the performance of the proposed tool and the ones mentioned above. Pr[m] outperformed the existing methods in terms of predictive quality and performance. The proposed approach is hosted at https://bioserver.iiita.ac.in/Pr[m].

Prediction of piRNAs and their function based on discriminative intelligent model using hybrid features into Chou’s PseKNC

Piwi interacting RNA (piRNA) is a recognized group of small non-coding RNA molecules. The piRNA molecules are associated with multiple tumors type diagnosis and drug development. It is also linked to regulate gene expression, suppressing transposon and maintains genome integrity. Due to a vital role of piRNAs in biology, the identification of piRNAs and their function has become an important area of research in computational biology. This paper proposes a robust two-layer predictor called “piRNA (2L)-PseKNC” to improve prediction of piRNAs and their function. The proposed predictor employing hybrid pseudo-K-tuple nucleotide composition (PseKNC) for sequence formulation, unsupervised principal component analysis (PCA) algorithm for discriminant feature selection and deep neural network (DNN) as a classifier. The proposed predictor was designed based on two layers approach. The first layer predicts either the encoded sequence belongs to piRNA or non-piRNA sequence and the second layer predict the selected piRNA sequence is functional piRNA or non-functional piRNA sequence. The overall accuracies of the proposed model using 5-fold cross validation test were 94.73% at first layer and 85.21% at second layer, surpassing the existing predictors with accuracies improvement 7.59% and 2.81% at the first layer and at the second layer respectively.

What Is the Role for Algorithmics and Computational Biology in Responding to the COVID-19 Pandemic?

We asked speakers from the Annual International Conference on Research in Computational Molecular Biology (RECOMB) about how computational biology as a discipline is being affected by COVID-19 and how the expertise of their community can help in the global response to the pandemic.

Optimizing High-Performance Computing Systems for Biomedical Workloads.

The productivity of computational biologists is limited by the speed of their workflows and subsequent overall job throughput. Because most biomedical researchers are focused on better understanding scientific phenomena rather than developing and optimizing code, a computing and data system implemented in an adventitious and/or non-optimized manner can impede the progress of scientific discovery. In our experience, most computational, life-science applications do not generally leverage the full capabilities of high-performance computing, so tuning a system for these applications is especially critical. To optimize a system effectively, systems staff must understand the effects of the applications on the system. Effective stewardship of the system includes an analysis of the impact of the applications on the compute cores, file system, resource manager and queuing policies. The resulting improved system design, and enactment of a sustainability plan, help to enable a long-term resource for productive computational and data science. We present a case study of a typical biomedical computational workload at a leading academic medical center supporting over $100 million per year in computational biology research. Over the past eight years, our high-performance computing system has enabled over 900 biomedical publications in four major areas: genetics and population analysis, gene expression, machine learning, and structural and chemical biology. We have upgraded the system several times in response to trends, actual usage, and user feedback. Major components crucial to this evolution include scheduling structure and policies, memory size, compute type and speed, parallel file system capabilities, and deployment of cloud technologies. We evolved a 70 teraflop machine to a 1.4 petaflop machine in seven years and grew our user base nearly 10-fold. For long-term stability and sustainability, we established a chargeback fee structure. Our overarching guiding principle for each progression has been to increase scientific throughput and enable enhanced scientific fidelity with minimal impact to existing user workflows or code. This highly-constrained system optimization has presented unique challenges, leading us to adopt new approaches to provide constructive pathways forward. We share our practical strategies resulting from our ongoing growth and assessments.

Preface Special Issue: RECOMB 2018.

Journal of Computational BiologyVol. 27, No. 3 PrefaceFree AccessPreface Special Issue: RECOMB 2018Sorin IstrailSorin IstrailSorin Istrail, PhD, Co-Editor-in-Chief Search for more papers by this authorPublished Online:11 Mar 2020https://doi.org/10.1089/cmb.2020.29026.siAboutSectionsPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail RECOMB, the International Conference on Research in Computational Molecular Biology, was held on April 21–24, 2018, in Paris (https://recomb2018.fr/). This was the conference's 22nd edition. The conference is a series of well-established scientific conferences bridging the areas of computational, mathematical, statistical, and biological sciences. The conference featured keynote talks by pre-eminent scientists in life sciences, presentations of groundbreaking research in computational biology, selected over the course of a highly rigorous peer-review process, and poster sessions on the latest research progress. RECOMB 2018 was located at the International Conference Center (CICSU) of the Pierre and Marie Curie Campus of Sorbonne University in the historic Jussieu district of Paris. The conference was attended by 510 participants.Following a tradition started by Michael Waterman at the first conference, RECOMB 1997, the Journal of Computational Biology (JCB) has published every year the RECOMB/JCB Special Issue containing a set of articles in final form presented at that year's RECOMB conference.The present volume is the 2018 RECOMB/JCB Special Issue and contains 13 articles presented at the conference.FiguresReferencesRelatedDetails Volume 27Issue 3Mar 2020 InformationCopyright 2020, Mary Ann Liebert, Inc., publishersTo cite this article:Sorin Istrail.Preface Special Issue: RECOMB 2018.Journal of Computational Biology.Mar 2020.301-301.http://doi.org/10.1089/cmb.2020.29026.siPublished in Volume: 27 Issue 3: March 11, 2020PDF download

Extension of the Quantum Universal Exchange Language to precision medicine and drug lead discovery. Preliminary example studies using the mitochondrial genome

The Quantum Universal Exchange Language (Q-UEL) based on Dirac notation and algebra from quantum mechanics, along with its associated data mining and Hyperbolic Dirac Net (HDN) for probabilistic inference, has proven to be a useful architectural principle for knowledge management, analysis and prediction systems in medicine. It has been described in several papers; here is described its extension to clinical genomics and precision medicine. Two use cases are studied: (a) bioinformatics in clinical decision support especially for risk for type 2 diabetes using mitochondrial patient DNA sequences, and (b) bioinformatics and computational biology (conformational) research examples related to drug discovery involving the recently discovered class of mitochondrial derived peptides (MDPs). MDPs were surprising when first discovered as coded in small open reading frames (sORFs), and are emerging as having a fundamental role in metabolic control, longevity and disease. This project originally represented a language specification study relating to what information related to genomics is essential or useful to carry, and what processing will be needed. However, novel aspects introduced or discovered include the HDN-like neural nets and their use, along with more established methods, for prediction of type 2 diabetes, and in particular for proposals for over 80 natural MDPs most of which that have not previously been described at the time of the study, as potential drug lead targets. Also, use of many medical records with simulated joining of mtDNA as performance tests led to some insightful observations regarding the behavior of HDN predictions where independent factors are involved.

Cardioinformatics: the nexus of bioinformatics and precision cardiology.

Cardiovascular disease (CVD) is the leading cause of death worldwide, causing over 17 million deaths per year, which outpaces global cancer mortality rates. Despite these sobering statistics, most bioinformatics and computational biology research and funding to date has been concentrated predominantly on cancer research, with a relatively modest footprint in CVD. In this paper, we review the existing literary landscape and critically assess the unmet need to further develop an emerging field at the multidisciplinary interface of bioinformatics and precision cardiovascular medicine, which we refer to as 'cardioinformatics'.

Donald A. B. Lindberg (1933-2019).

Visionary medical informaticist who laid the groundwork for PubMed

Advancing computational biology and bioinformatics research through open innovation competitions.

Open data science and algorithm development competitions offer a unique avenue for rapid discovery of better computational strategies. We highlight three examples in computational biology and bioinformatics research in which the use of competitions has yielded significant performance gains over established algorithms. These include algorithms for antibody clustering, imputing gene expression data, and querying the Connectivity Map (CMap). Performance gains are evaluated quantitatively using realistic, albeit sanitized, data sets. The solutions produced through these competitions are then examined with respect to their utility and the prospects for implementation in the field. We present the decision process and competition design considerations that lead to these successful outcomes as a model for researchers who want to use competitions and non-domain crowds as collaborators to further their research.

Connectivity problems on heterogeneous graphs

BackgroundNetwork connectivity problems are abundant in computational biology research, where graphs are used to represent a range of phenomena: from physical interactions between molecules to more abstract relationships such as gene co-expression. One common challenge in studying biological networks is the need to extract meaningful, small subgraphs out of large databases of potential interactions. A useful abstraction for this task turned out to be the Steiner Network problems: given a reference “database” graph, find a parsimonious subgraph that satisfies a given set of connectivity demands. While this formulation proved useful in a number of instances, the next challenge is to account for the fact that the reference graph may not be static. This can happen for instance, when studying protein measurements in single cells or at different time points, whereby different subsets of conditions can have different protein milieu.Results and discussionWe introduce the condition Steiner Network problem in which we concomitantly consider a set of distinct biological conditions. Each condition is associated with a set of connectivity demands, as well as a set of edges that are assumed to be present in that condition. The goal of this problem is to find a minimal subgraph that satisfies all the demands through paths that are present in the respective condition. We show that introducing multiple conditions as an additional factor makes this problem much harder to approximate. Specifically, we prove that for C conditions, this new problem is NP-hard to approximate to a factor of C - epsilon , for every C ge 2 and epsilon > 0, and that this bound is tight. Moving beyond the worst case, we explore a special set of instances where the reference graph grows monotonically between conditions, and show that this problem admits substantially improved approximation algorithms. We also developed an integer linear programming solver for the general problem and demonstrate its ability to reach optimality with instances from the human protein interaction network.ConclusionOur results demonstrate that in contrast to most connectivity problems studied in computational biology, accounting for multiplicity of biological conditions adds considerable complexity, which we propose to address with a new solver. Importantly, our results extend to several network connectivity problems that are commonly used in computational biology, such as Prize-Collecting Steiner Tree, and provide insight into the theoretical guarantees for their applications in a multiple condition setting.

IEnhancer-5Step: Identifying enhancers using hidden information of DNA sequences via Chou's 5-step rule and word embedding

An enhancer is a short (50–1500bp) region of DNA that plays an important role in gene expression and the production of RNA and proteins. Genetic variation in enhancers has been linked to many human diseases, such as cancer, disorder or inflammatory bowel disease. Due to the importance of enhancers in genomics, the classification of enhancers has become a popular area of research in computational biology. Despite the few computational tools employed to address this problem, their resulting performance still requires improvements. In this study, we treat enhancers by the word embeddings, including sub-word information of its biological words, which then serve as features to be fed into a support vector machine algorithm to classify them. We present iEnhancer-5Step, a web server containing two-layer classifiers to identify enhancers and their strength. We are able to attain an independent test accuracy of 79% and 63.5% in the two layers, respectively. Compared to current predictors on the same dataset, our proposed method is able to yield superior performance as compared to the other methods. Moreover, this study provides a basis for further research that can enrich the field of applying natural language processing techniques in biological sequences. iEnhancer-5Step is freely accessible via http://biologydeep.com/fastenc/.

Graph pruning based approach for inferring disease causing genes and associated pathways

The problem of inferring disease causing genes and dysregulated pathways has obtained a vital position in computational biology research. But, the huge size of the biological network makes this process computationally challenging. Here, we tackle the problem of inferring disease causing genes and associated pathways using graph pruning techniques which focus on the improvement in accuracy of results in reasonable execution time and fetching more causal genes and their pathways. Experimentation of the proposed approach and the reported approaches in literature was done on real biological data. More efficient results in terms of accuracy and execution time based on benchmark datasets were obtained as its outcome. If the function of the newly identified genes/pathways in the disease states could be validated biologically, for any unknown influences in the disease development, it would significantly affect our effort to design new drug targets and defeat the diseases.

"Reproducible" Research in Mathematical Sciences Requires Changes in our Peer Review Culture and Modernization of our Current Publication Approach.

The nature of scientific research in mathematical and computational biology allows editors and reviewers to evaluate the findings of a scientific paper. Replication of a research study should be the minimum standard for judging its scientific claims and considering it for publication. This requires changes in the current peer review practice and a strict adoption of a replication policy similar to those adopted in experimental fields such as organic synthesis. In the future, the culture of replication can be easily adopted by publishing papers through dynamic computational notebooks combining formatted text, equations, computer algebra and computer code.

Resolving "orphaned" non-specific structures using machine learning and natural language processing methods.

Scholarly publications of biodiversity literature contain a vast amount of information in human readable format. The detailed morphological descriptions in these publications contain rich information that can be extracted to facilitate analysis and computational biology research. However, the idiosyncrasies of morphological descriptions still pose a number of challenges to machines. In this work, we demonstrate the use of two different approaches to resolve meronym (i.e. part-of) relations between anatomical parts and their anchor organs, including a syntactic rule-based approach and a SVM-based (support vector machine) method. Both methods made use of domain ontologies. We compared the two approaches with two other baseline methods and the evaluation results show the syntactic methods (92.1% F1 score) outperformed the SVM methods (80.7% F1 score) and the part-of ontologies were valuable knowledge sources for the task. It is notable that the mistakes made by the two approaches rarely overlapped. Additional tests will be conducted on the development version of the Explorer of Taxon Concepts toolkit before we make the functionality publicly available. Meanwhile, we will further investigate and leverage the complementary nature of the two approaches to further drive down the error rate, as in practical application, even a 1% error rate could lead to hundreds of errors.

Preface: Selected Articles from RECOMB 2017.

Preface: Selected Articles from RECOMB 2017.

Generalizing Gillespie's Direct Method to Enable Network-Free Simulations.

Gillespie's direct method for stochastic simulation of chemical kinetics is a staple of computational systems biology research. However, the algorithm requires explicit enumeration of all reactions and all chemical species that may arise in the system. In many cases, this is not feasible due to the combinatorial explosion of reactions and species in biological networks. Rule-based modeling frameworks provide a way to exactly represent networks containing such combinatorial complexity, and generalizations of Gillespie's direct method have been developed as simulation engines for rule-based modeling languages. Here, we provide both a high-level description of the algorithms underlying the simulation engines, termed network-free simulation algorithms, and how they have been applied in systems biology research. We also define a generic rule-based modeling framework and describe a number of technical details required for adapting Gillespie's direct method for network-free simulation. Finally, we briefly discuss potential avenues for advancing network-free simulation and the role they continue to play in modeling dynamical systems in biology.

A safe and complete algorithm for metagenomic assembly

BackgroundReconstructing the genome of a species from short fragments is one of the oldest bioinformatics problems. Metagenomic assembly is a variant of the problem asking to reconstruct the circular genomes of all bacterial species present in a sequencing sample. This problem can be naturally formulated as finding a collection of circular walks of a directed graph G that together cover all nodes, or edges, of G.ApproachWe address this problem with the “safe and complete” framework of Tomescu and Medvedev (Research in computational Molecular biology—20th annual conference, RECOMB 9649:152–163, 2016). An algorithm is called safe if it returns only those walks (also called safe) that appear as subwalk in all metagenomic assembly solutions for G. A safe algorithm is called complete if it returns all safe walks of G.ResultsWe give graph-theoretic characterizations of the safe walks of G, and a safe and complete algorithm finding all safe walks of G. In the node-covering case, our algorithm runs in time O(m^2 + n^3), and in the edge-covering case it runs in time O(m^2n); n and m denote the number of nodes and edges, respectively, of G. This algorithm constitutes the first theoretical tight upper bound on what can be safely assembled from metagenomic reads using this problem formulation.