Background Self-interacting proteins (SIPs), two or more copies of the protein that can interact with each other expressed by one gene, play a central role in the regulation of most living cells and cellular functions. Although numerous SIPs data can be provided by using high-throughput experimental techniques, there are still several shortcomings such as in time-consuming, costly, inefficient, and inherently high in false-positive rates, for the experimental identification of SIPs even nowadays. Therefore, it is more and more significant how to develop efficient and accurate automatic approaches as a supplement of experimental methods for assisting and accelerating the study of predicting SIPs from protein sequence information. Results In this paper, we present a novel framework, termed GLCM-WSRC (gray level co-occurrence matrix-weighted sparse representation based classification), for predicting SIPs automatically based on protein evolutionary information from protein primary sequences. More specifically, we firstly convert the protein sequence into Position Specific Scoring Matrix (PSSM) containing protein sequence evolutionary information, exploiting the Position Specific Iterated BLAST (PSI-BLAST) tool. Secondly, using an efficient feature extraction approach, i.e., GLCM, we extract abstract salient and invariant feature vectors from the PSSM, and then perform a pre-processing operation, the adaptive synthetic (ADASYN) technique, to balance the SIPs dataset to generate new feature vectors for classification. Finally, we employ an efficient and reliable WSRC model to identify SIPs according to the known information of self-interacting and non-interacting proteins. Conclusions Extensive experimental results show that the proposed approach exhibits high prediction performance with 98.10% accuracy on the yeast dataset, and 91.51% accuracy on the human dataset, which further reveals that the proposed model could be a useful tool for large-scale self-interacting protein prediction and other bioinformatics tasks detection in the future.
Background Extraction of drug drug interactions from biomedical literature and other textual data is an important component to monitor drug-safety and this has attracted attention of many researchers in healthcare. Existing works are more pivoted around relation extraction using bidirectional long short-term memory networks (BiLSTM) and BERT model which does not attain the best feature representations. Results Our proposed DDI (drug drug interaction) prediction model provides multiple advantages: (1) The newly proposed attention vector is added to better deal with the problem of overlapping relations, (2) The molecular structure information of drugs is integrated into the model to better express the functional group structure of drugs, (3) We also added text features that combined the T-distribution and chi-square distribution to make the model more focused on drug entities and (4) it achieves similar or better prediction performance (F-scores up to 85.16%) compared to state-of-the-art DDI models when tested on benchmark datasets. Conclusions Our model that leverages state of the art transformer architecture in conjunction with multiple features can bolster the performances of drug drug interation tasks in the biomedical domain. In particular, we believe our research would be helpful in identification of potential adverse drug reactions.
BACKGROUND:Protein ubiquitination catalyzed by E3 ubiquitin ligases play important modulatory roles in various biological processes. With the emergence of high-throughput mass spectrometry technology, the proteomics research community embraced the development of numerous experimental methods for the determination of ubiquitination sites. The result is an accumulation of ubiquitinome data, coupled with a lack of available resources for investigating the regulatory networks among E3 ligases and ubiquitinated proteins. In this study, by integrating existing ubiquitinome data, experimentally validated E3 ligases and established protein-protein interactions, we have devised a strategy to construct a comprehensive map of protein ubiquitination networks.RESULTS:In total, 41,392 experimentally verified ubiquitination sites from 12,786 ubiquitinated proteins of humans have been obtained for this study. Additional 494 E3 ligases along with 1220 functional annotations and 28588 protein domains were manually curated. To characterize the regulatory networks among E3 ligases and ubiquitinated proteins, a well-established network viewer was utilized for the exploration of ubiquitination networks from 40892 protein-protein interactions. The effectiveness of the proposed approach was demonstrated in a case study examining E3 ligases involved in the ubiquitination of tumor suppressor p53. In addition to Mdm2, a known regulator of p53, the investigation also revealed other potential E3 ligases that may participate in the ubiquitination of p53.CONCLUSION:Aside from the ability to facilitate comprehensive investigations of protein ubiquitination networks, by integrating information regarding protein-protein interactions and substrate specificities, the proposed method could discover potential E3 ligases for ubiquitinated proteins. Our strategy presents an efficient means for the preliminary screen of ubiquitination networks and overcomes the challenge as a result of limited knowledge about E3 ligase-regulated ubiquitination.
The methylotrophic yeast Pichia pastoris has gained much attention during the last decade as a platform for producing heterologous recombinant proteins of pharmaceutical importance, due to its ability to reproduce post-translational modification similar to higher eukaryotes. With the recent release of the full genome sequence for P. pastoris, in-depth study of its functions has become feasible. Here we present the first reconstruction of the genome-scale metabolic model of the eukaryote P. pastoris type strain DSMZ 70382, PpaMBEL1254, consisting of 1254 metabolic reactions and 1147 metabolites compartmentalized into eight different regions to represent organelles. Additionally, equations describing the production of two heterologous proteins, human serum albumin and human superoxide dismutase, were incorporated. The protein-producing model versions of PpaMBEL1254 were then analyzed to examine the impact on oxygen limitation on protein production.
To establish itself within the host system, Mycobacterium tuberculosis (Mtb) has formulated various means of attacking the host system. One such crucial strategy is the exploitation of the iron resources of the host system. Obtaining and maintaining the required concentration of iron becomes a matter of contest between the host and the pathogen, both trying to achieve this through complex molecular networks. The extent of complexity makes it important to obtain a systems perspective of the interplay between the host and the pathogen with respect to iron homeostasis. We have reconstructed a systems model comprising 92 components and 85 protein-protein or protein-metabolite interactions, which have been captured as a set of 194 rules. Apart from the interactions, these rules also account for protein synthesis and decay, RBC circulation and bacterial production and death rates. We have used a rule-based modelling approach, Kappa, to simulate the system separately under infection and non-infection conditions. Various perturbations including knock-outs and dual perturbation were also carried out to monitor the behavioral change of important proteins and metabolites. From this, key components as well as the required controlling factors in the model that are critical for maintaining iron homeostasis were identified. The model is able to re-establish the importance of iron-dependent regulator (ideR) in Mtb and transferrin (Tf) in the host. Perturbations, where iron storage is increased, appear to enhance nutritional immunity and the analysis indicates how they can be harmful for the host. Instead, decreasing the rate of iron uptake by Tf may prove to be helpful. Simulation and perturbation studies help in identifying Tf as a possible drug target. Regulating the mycobactin (myB) concentration was also identified as a possible strategy to control bacterial growth. The simulations thus provide significant insight into iron homeostasis and also for identifying possible drug targets for tuberculosis.
Protein subcellular localization is concerned with predicting the location of a protein within a cell using computational methods. The location information can indicate key functionalities of proteins. Thus, accurate prediction of subcellular localizations of proteins can help the prediction of protein functions and genome annotations, as well as the identification of drug targets. Machine learning methods such as Support Vector Machines (SVMs) have been used in the past for the problem of protein subcellular localization, but have been shown to suffer from a lack of annotated training data in each species under study. To overcome this data sparsity problem, we observe that because some of the organisms may be related to each other, there may be some commonalities across different organisms that can be discovered and used to help boost the data in each localization task. In this paper, we formulate protein subcellular localization problem as one of multitask learning across different organisms. We adapt and compare two specializations of the multitask learning algorithms on 20 different organisms. Our experimental results show that multitask learning performs much better than the traditional single-task methods. Among the different multitask learning methods, we found that the multitask kernels and supertype kernels under multitask learning that share parameters perform slightly better than multitask learning by sharing latent features. The most significant improvement in terms of localization accuracy is about 25 percent. We find that if the organisms are very different or are remotely related from a biological point of view, then jointly training the multiple models cannot lead to significant improvement. However, if they are closely related biologically, the multitask learning can do much better than individual learning.
Such difficult analyses as disease association studies, which aim at mapping genetic variants underlying complex human diseases, rely on high-throughput genotyping techniques. However, a shortcoming of these techniques is the generation of missing calls. Computational inference of missing data represents a challenging alternative to genotyping again the missing regions. In this paper, we present SNPShuttle, an algorithm designed to improve inference accuracy with respect to a former software designed by Roberts and co-authors, NPUTE (Roberts et al., 2007). Given an SNP panel, NPUTE algorithm infers missing data through a single scan of the panel, exploiting local similarity within sliding windows. Instead, SNPShuttle scans an SNP panel in an iterative bi-directional way, to resolve missing data with more confidence. We evaluate the accuracy gain, systematically comparing a variant of NPUTE and SNPShuttle for controlled missing data percentages (pmiss) ranging from 5 to 30%. The corresponding benchmarks are built from the high resolution map of mouse strains made available by the Perlegen Project. In all cases (all missing data percentages, all 20 chromosomes studied), SNPShuttle is shown to bring a gain of accuracy. For pmiss percentages comprised between 5 and 15%, the average gain ranges between 1.56 and 1.81%. All missing data percentages confounded, accuracies obtained with the original method range in interval 92%-96.5%, for all 20 chromosomes analysed. In all cases, we show that SNPShuttle always increases the accuracy, by 0.88 up to 1.81%. We show that scanning an SNP panel in an iterative bi-directional way, only fixing SNPs inferred identically through two successive scans, brings invaluable accuracy gain with regard to the original method.
A compact and low profile document handling unit with short and simple document recirculation paths for recirculating a set of original document sheets to and from one side of the platen of a copier with or without inversion for selective repeated copying of one or both sides of the documents, in which the selectable document inverter in the document recirculation path includes a generally planar document sheet reversing chute which is generally parallel to and closely overlying the platen and a sheet reversing feeder adjacent the same side of the platen for feeding the document sheets into the reversing chute directly from the platen, but inverted, and for feeding them out of the reversing chute to a restacking tray at that same side of the platen without inversion.
The intra-nuclear organisation of proteins is based on possibly transient interactions with morphologically defined compartments like the nucleolus. The fluidity of trafficking challenges the development of models that accurately identify compartment membership for novel proteins. A growing inventory of nucleolar proteins is here used to train a support-vector machine to recognise sequence features that allow the automatic assignment of compartment membership. We explore a range of sequence-kernels and find that while some success is achieved with a profile-based local alignment kernel, the problem is ill-suited to a standard compartment-classification approach.
Primer Approximation Multiplex PCR (PAMP) is a recently introduced experimental technique for detecting large-scale cancer genome lesions such as inversions and deletions from heterogeneous samples containing a mixture of cancer and normal cells. In this paper we give integer linear programming formulations for the problem of selecting sets of PAMP primers that minimize detection failure probability. We also show that PAMP primer selection for detection of anchored deletions cannot be approximated within a factor of 2 , and give a 2-approximation algorithm for a special case of the problem. Experimental results show that our ILP formulations can be used to optimally solve medium size instances of the inversion detection problem, and that heuristics based on iteratively solving ILP formulations for a one-sided version of the problem give near-optimal solutions for anchored deletion detection with highly scalable runtime.
National Institute of Advanced Industrial Science and Technology,Computational Biology Research Center,2-42 Aomi, Koto-ku, Tokyo 135-0064, JapanGenomic sequence alignment is a powerful tool for finding com mon subsequence patterns sharedby the input sequences and identifying evolutionary relationships between the species. However, therunning time and space requirement of genome alignment have often been very extensive. In thisresearch, we propose a novel algorithm called Coarse-Grained AlignmenT (CGAT) algorithm, forreducing computational complexity necessary for cross-species whole genome sequence alignment.The CGAT first divides the input sequences into ”blocks” with a fixed length and aligns these blocksto each other. The generated block-level alignment is then refined at the nucleotide level. This two-stepprocedure can drastically reduce the overall computational time and space necessary for an alignment.In this paper, we show the effectiveness of the proposed algorithm by applying it to whole genomesequences of several bacteria.Keywords: Genome Alignment; Multiple Alignment; Sequence Analysis; Comparative Genomics.
Series on Advances in Bioinformatics and Computational BiologyProceedings of the 6th Asia-Pacific Bioinformatics Conference, pp. 241-250 (2007) No AccessPHYLOGENETIC RECONSTRUCTION FROM COMPLETE GENE ORDERS OF WHOLE GENOMESKRISTER M. SWENSON, WILLIAM ARNDT, JIJUN TANG, and BERNARD M. E. MORETKRISTER M. SWENSONSchool of Computer and Communication Sciences, Swiss Federal Institute of Technology (EPFL), EPFL 1C LCBB, INJ 232, Station 14, CH-1015 Lausanne, Switzerland, WILLIAM ARNDTDepartment of Computer Science & Engineering, University of South Carolina, Columbia, SC 29208, USA, JIJUN TANGDepartment of Computer Science & Engineering, University of South Carolina, Columbia, SC 29208, USA, and BERNARD M. E. MORETSchool of Computer and Communication Sciences, Swiss Federal Institute of Technology (EPFL), EPFL 1C LCBB, INJ 230, Station 14, CH-1015 Lausanne, SwitzerlandSwiss Institute of Bioinformatics, Switzerlandhttps://doi.org/10.1142/9781848161092_0026Cited by:6 PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Abstract: Rapidly increasing numbers of organisms have been completely sequenced and most of their genes identified; homologies among these genes are also getting established. It thus has become possible to represent whole genomes as ordered lists of gene identifiers and to study the evolution of these entities through computational means, in systematics as well as in comparative genomics. While dealing with rearrangements is nontrivial, the biggest stumbling block remains gene duplication and losses, leading to considerable difficulties in determining orthologs among gene families—all the more since orthology determination has a direct impact on the selection of rearrangements. None of the existing phylogenetic reconstruction methods that use gene orders is able to exploit the information present in complete gene families—most assume singleton families and equal gene content, limiting the evolutionary operations to rearrangements, while others make it so by eliminating nonshared genes and selecting one exemplar from each gene family. In this work, we leverage our past work on genomic distances, on tight bounding of parsimony scores through linear programming, and on divide-and-conquer methods for large-scale reconstruction to build the first computational approach to phylogenetic reconstruction from complete gene order data, taking into account not only rearrangements, but also duplication and loss of genes. Our approach can handle mulitchromosomal data and gene families of arbitrary sizes and scale up to hundreds of genomes through the use of disk-covering methods. We present experimental results on simulated unichromosomal genomes in a range of sizes consistent with prokaryotes. Our results confirm that equalizing gene content, as done in existing phylogenetic tools, discards important phylogenetic information; in particular, our approach easily outperforms that most commonly referenced tool, MGR, often returning trees with less that one quarter of the errors found in the MGR trees. Keywords: phylogenetic reconstructionwhole-genome datagenomic distancegene inversiongene duplicationgene loss FiguresReferencesRelatedDetailsCited By 6Ancestral Genome ReconstructionJijun Tang9 April 2019Sorting Linear Genomes with Rearrangements and IndelsMarilia D. V. Braga and Jens Stoye1 May 2015 | IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 12, No. 3Restricted DCJ-Indel Model RevisitedMarília D. V. Braga and Jens Stoye1 Jan 2013A Hybrid Optimization Algorithm for the Multi-Chromosomal Reversal Median ProblemYi Zhang and Meng Zhang1 December 2011 | Applied Mechanics and Materials, Vol. 145The Kernel of Maximum Agreement SubtreesKrister M. Swenson, Eric Chen, Nicholas D. Pattengale and David Sankoff1 Jan 2011A Branch-and-Bound Method for the Multichromosomal Reversal Median ProblemMeng Zhang, William Arndt and Jijun Tang Proceedings of the 6th Asia-Pacific Bioinformatics ConferenceMetrics History Keywordsphylogenetic reconstructionwhole-genome datagenomic distancegene inversiongene duplicationgene lossPDF download
We have developed a generic framework for combining introns from genomicly aligned expressed–sequence–tag clusters with a set of exon predictions to produce alternative transcript predictions. Our current implementation uses ASPIC to generate alternative transcripts from EST mappings. Introns from ASPIC and a set of gene predictions from many diverse gene prediction programs are given to the gene prediction combiner GenePC which then generates alternative consensus splice forms. We evaluated our method on the ENCODE regions of the human genome. In general we see a marked improvement in transcript-level sensitivity due to the fact that more than one transcript per gene may now be predicted. GenePC, which alone is highly specific at the transcript level, balances the lower specificity of ASPIC.
DNA replication is a key process in cell division cycle. It is initiated in coordinated manner in several species. To understand the DNA replication in a species one needs to measure the half replication timing (or replication timing) and the efficiency of replication which vary across genome in higher eukaryotes. In the previous studies, no direct assessment of replication efficiency on a genomic scale was performed while the replication timing was indirectly assessed using average DNA. In this paper, we present a first-ever-method of directly measuring both half replication timing and efficiency simultaneously from a single DNA microarray time-course data. We achieve it by fitting the so called near-sigmoid model to each locus of the DNA. We use this model apply S. pombe DNA replication microarray data and show that it is effective for genome-scale replication timing and efficiency profiling studies.
