for Journals by Title or ISSN
for Articles by Keywords
help
  Subjects -> ENGINEERING (Total: 2167 journals)
    - CHEMICAL ENGINEERING (184 journals)
    - CIVIL ENGINEERING (168 journals)
    - ELECTRICAL ENGINEERING (94 journals)
    - ENGINEERING (1173 journals)
    - ENGINEERING MECHANICS AND MATERIALS (355 journals)
    - HYDRAULIC ENGINEERING (55 journals)
    - INDUSTRIAL ENGINEERING (57 journals)
    - MECHANICAL ENGINEERING (81 journals)

CHEMICAL ENGINEERING (184 journals)                  1 2     

AATCC Journal of Research     Full-text available via subscription   (Followers: 3)
Acta Crystallographica Section B: Structural Science, Crystal Engineering and Materials     Hybrid Journal   (Followers: 4)
Acta Polymerica     Hybrid Journal   (Followers: 6)
Additives for Polymers     Full-text available via subscription   (Followers: 20)
Adhesion Adhesives & Sealants     Hybrid Journal   (Followers: 5)
Advanced Chemical Engineering Research     Open Access   (Followers: 9)
Advanced Powder Technology     Hybrid Journal   (Followers: 16)
Advances in Applied Ceramics     Partially Free   (Followers: 3)
Advances in Chemical Engineering     Full-text available via subscription   (Followers: 17)
Advances in Chemical Engineering and Science     Open Access   (Followers: 24)
Advances in Polymer Technology     Hybrid Journal   (Followers: 11)
African Journal of Pure and Applied Chemistry     Open Access   (Followers: 5)
Annual Review of Analytical Chemistry     Full-text available via subscription   (Followers: 9)
Annual Review of Chemical and Biomolecular Engineering     Full-text available via subscription   (Followers: 10)
Anti-Corrosion Methods and Materials     Hybrid Journal   (Followers: 4)
Applied Petrochemical Research     Open Access   (Followers: 3)
Asia-Pacific Journal of Chemical Engineering     Hybrid Journal   (Followers: 6)
Biochemical Engineering Journal     Hybrid Journal   (Followers: 9)
Biofuel Research Journal     Open Access   (Followers: 2)
Biomass Conversion and Biorefinery     Partially Free   (Followers: 6)
Brazilian Journal of Chemical Engineering     Open Access   (Followers: 3)
Bulletin of the Chemical Society of Ethiopia     Open Access   (Followers: 2)
Carbohydrate Polymers     Hybrid Journal   (Followers: 9)
Catalysts     Open Access   (Followers: 8)
Chemical and Engineering News     Free   (Followers: 7)
Chemical and Materials Engineering     Open Access   (Followers: 1)
Chemical and Petroleum Engineering     Hybrid Journal   (Followers: 11)
Chemical and Process Engineering     Open Access   (Followers: 4)
Chemical and Process Engineering Research     Open Access   (Followers: 6)
Chemical Communications     Full-text available via subscription   (Followers: 33)
Chemical Engineering & Technology     Hybrid Journal   (Followers: 25)
Chemical Engineering and Processing: Process Intensification     Hybrid Journal   (Followers: 10)
Chemical Engineering and Science     Open Access   (Followers: 3)
Chemical Engineering Communications     Hybrid Journal   (Followers: 10)
Chemical Engineering Journal     Hybrid Journal   (Followers: 23)
Chemical Engineering Research and Design     Hybrid Journal   (Followers: 19)
Chemical Engineering Research Bulletin     Open Access   (Followers: 1)
Chemical Engineering Science     Hybrid Journal   (Followers: 17)
Chemical Geology     Hybrid Journal   (Followers: 10)
Chemical Papers     Hybrid Journal   (Followers: 3)
Chemical Product and Process Modeling     Hybrid Journal   (Followers: 3)
Chemical Reviews     Full-text available via subscription   (Followers: 156)
Chemical Society Reviews     Full-text available via subscription   (Followers: 34)
Chemical Technology     Open Access   (Followers: 5)
ChemInform     Hybrid Journal   (Followers: 3)
Chemistry & Industry     Hybrid Journal   (Followers: 2)
Chemistry Central Journal     Open Access   (Followers: 5)
Chemistry of Materials     Full-text available via subscription   (Followers: 138)
Chemometrics and Intelligent Laboratory Systems     Hybrid Journal   (Followers: 6)
ChemSusChem     Hybrid Journal   (Followers: 8)
Chinese Chemical Letters     Full-text available via subscription   (Followers: 2)
Chinese Journal of Chemical Engineering     Full-text available via subscription   (Followers: 3)
Chinese Journal of Chemical Physics     Hybrid Journal   (Followers: 1)
Coke and Chemistry     Hybrid Journal  
Coloration Technology     Hybrid Journal   (Followers: 1)
Computational Biology and Chemistry     Hybrid Journal   (Followers: 8)
Computer Aided Chemical Engineering     Full-text available via subscription   (Followers: 2)
Computers & Chemical Engineering     Hybrid Journal   (Followers: 8)
CORROSION     Full-text available via subscription   (Followers: 3)
Corrosion Engineering, Science and Technology     Hybrid Journal   (Followers: 22)
Corrosion Reviews     Hybrid Journal   (Followers: 4)
Crystal Research and Technology     Hybrid Journal   (Followers: 2)
Current Opinion in Chemical Engineering     Open Access   (Followers: 3)
Education for Chemical Engineers     Hybrid Journal   (Followers: 4)
Ekologia : The Journal of Institute of Landscape Ecology of Slovak Academy of Sciences     Open Access  
Eksergi     Open Access  
Emerging Trends in Chemical Engineering     Full-text available via subscription  
European Polymer Journal     Hybrid Journal   (Followers: 43)
Fibers and Polymers     Full-text available via subscription   (Followers: 3)
Fluorescent Materials     Open Access  
Focusing on Modern Food Industry     Open Access   (Followers: 3)
Frontiers of Chemical Science and Engineering     Hybrid Journal   (Followers: 1)
Gels     Open Access  
Geochemistry International     Hybrid Journal  
Handbook of Powder Technology     Full-text available via subscription   (Followers: 3)
Heat Exchangers     Open Access   (Followers: 1)
High Performance Polymers     Hybrid Journal  
Indian Chemical Engineer     Hybrid Journal   (Followers: 3)
Indian Journal of Chemical Technology (IJCT)     Open Access   (Followers: 11)
Industrial & Engineering Chemistry     Full-text available via subscription   (Followers: 9)
Industrial & Engineering Chemistry Research     Full-text available via subscription   (Followers: 20)
Industrial Chemistry Library     Full-text available via subscription   (Followers: 4)
Info Chimie Magazine     Full-text available via subscription   (Followers: 2)
International Journal of Chemical and Petroleum Sciences     Open Access   (Followers: 1)
International Journal of Chemical Engineering     Open Access   (Followers: 7)
International Journal of Chemical Reactor Engineering     Hybrid Journal   (Followers: 3)
International Journal of Chemical Technology     Open Access   (Followers: 4)
International Journal of Chemoinformatics and Chemical Engineering     Full-text available via subscription   (Followers: 2)
International Journal of Food Science     Open Access   (Followers: 3)
International Journal of Industrial Chemistry     Open Access  
International Journal of Polymeric Materials     Hybrid Journal   (Followers: 3)
International Journal of Science and Engineering     Open Access   (Followers: 7)
International Journal of Waste Resources     Open Access   (Followers: 5)
Journal of Chemical Engineering & Process Technology     Open Access   (Followers: 3)
Journal of Applied Crystallography     Hybrid Journal   (Followers: 5)
Journal of Applied Electrochemistry     Hybrid Journal   (Followers: 11)
Journal of Applied Polymer Science     Hybrid Journal   (Followers: 126)
Journal of Biomaterials Science, Polymer Edition     Hybrid Journal   (Followers: 8)
Journal of Bioprocess Engineering and Biorefinery     Full-text available via subscription  
Journal of Chemical & Engineering Data     Full-text available via subscription   (Followers: 11)

        1 2     

Journal Cover   Computational Biology and Chemistry
  [SJR: 0.688]   [H-I: 43]   [8 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1476-9271
   Published by Elsevier Homepage  [2800 journals]
  • A Survey of Disease Connections for CD4+ T Cell Master Genes and Their
           Directly Linked Genes
    • Abstract: Publication date: Available online 25 August 2015
      Source:Computational Biology and Chemistry
      Author(s): Wentian Li, Jesús Espinal-Enríquez, Kim R Simpfendorfer, Enrique Hernández-Lemus
      Genome-wide association studies and other genetic analyses have identified a large number of genes and variants implicating a variety of disease etiological mechanisms. It is imperative for the study of human diseases to put these genetic findings into a coherent functional context. Here we use system biology tools to examine disease connections of five master genes for CD4+ T cell subtypes (TBX21, GATA3, RORC, BCL6, and FOXP3). We compiled a list of genes functionally interacting (protein-protein interaction, or by acting in the same pathway) with the master genes, then we surveyed the disease connections, either by experimental evidence or by genetic association. Embryonic lethal genes (also known as essential genes) are over-represented in master genes and their interacting genes (55% versus 40% in other genes). Transcription factors are significantly enriched among genes interacting with the master genes (63% versus 10% in other genes). Predicted haploinsufficiency is a feature of most these genes. Disease-connected genes are enriched in this list of genes: 42% of these genes have a disease connection according to Online Mendelian Inheritance in Man (OMIM) (versus 23% in other genes), and 74% are associated with some diseases or phenotype in a Genome Wide Association Study (GWAS) (versus 43% in other genes). Seemingly, not all of the diseases connected to genes surveyed were immune related, which may indicate pleiotropic functions of the master regulator genes and associated genes.


      PubDate: 2015-08-28T19:53:03Z
       
  • Transcriptional master regulator analysis in breast cancergenetic networks
    • Abstract: Publication date: Available online 22 August 2015
      Source:Computational Biology and Chemistry
      Author(s): Hugo Tovar, Rodrigo García-Herrera, Jesús Espinal-Enríquez, Enrique Hernández-Lemus
      Gene regulatory networks account for the delicate mechanisms that control gene expression. Under certain circumstances, gene regulatory programs may give rise to amplification cascades. Such transcriptional cascades are events in which activation of key-responsive transcription factors called master regulators trigger a series of gene expression events. The action of transcriptional master regulators is then important for the establishment of certain programs like cell development and differentiation. However, such cascades have also been related with the onset and maintenance of cancer phenotypes. Here we present a systematic implementation of a series of algorithms aimed at the inference of a gene regulatory network and analysis of transcriptional master regulators in the context of primary breast cancer cells. Such studies were performed in a highly curated database of 880 microarray gene expression experiments on biopsy-captured tissue corresponding to primary breast cancer and healthy controls. Biological function and biochemical pathway enrichment analyses were also performed to study the role that the processes controlled –at the transcriptional level– by such master regulators may have in relation to primary breast cancer. We found that transcription factors such as AGTR2, ZNF132, TFDP3 and others are master regulators in this gene regulatory network. Sets of genes controlled by these regulators are involved in processes that are well-known hallmarks of cancer. This kind of analyses may help to understand the most upstream events in the development of phenotypes, in particular, those regarding cancer biology.
      Graphical abstract image Highlights

      PubDate: 2015-08-24T19:45:31Z
       
  • Core and Peripheral connectivity based Cluster Analysis over PPI Network
    • Abstract: Publication date: Available online 24 August 2015
      Source:Computational Biology and Chemistry
      Author(s): Hasin A Ahmed, Dhruba K Bhattacharyya, Jugal K Kalita
      A number of methods have been proposed in the literature of Protein Protein Interraction (PPI) network analysis for detection of clusters in the network. Clusters are identified by these methods using various graph theoretic criteria. Most of these methods have been found time consuming due to involvement of preprocessing and post processing tasks. In addition, they do not achieve high precision and recall consistently and simultaneously. Moreover, the existing methods do not employ the idea of core-periphery structural pattern of protein complexes effectively to extract clusters. In this paper, we introduce a clustering method named CPCA based on a recent observation by researchers that a protein complex in a PPI network is arranged as a relatively dense core region and additional proteins weakly connected to the core. CPCA uses two connectivity criterion functions to identify core and peripheral regions of the cluster. To locate initial node of a cluster we introduce a measure called DNQ (Degree based Neighborhood Qualification) index that evaluates tendency of the node to be part of a cluster. CPCA performs well when compared with well known counterparts. Along with protein complex gold standards, a co-localization dataset has also been used for validation of the results.
      Graphical abstract image Highlights

      PubDate: 2015-08-24T19:45:31Z
       
  • Dynamics of p53 and Wnt cross talk
    • Abstract: Publication date: Available online 23 August 2015
      Source:Computational Biology and Chemistry
      Author(s): Md.Zubbair Malik, Shahnawaz Ali, Md. Jahoor Alam, Romana Ishrat, R.K. Brojen Singh
      We present the mechanism of interaction of Wnt network module, which is responsible for periodic sometogenesis, with p53 regulatory network, which is one of the main regulators of various cellular functions, and switching of various oscillating states by investigating p53−Wnt model. The variation in Nutlin concentration in p53 regulating network drives the Wnt network module to different states, stabilized, damped and sustain oscillation states, and even to cycle arrest. Similarly, the change in Axin2 concentration in Wnt could able to modulate the p53 dynamics at these states. We then solve the set of coupled ordinary differential equations of the model using quasi steady state approximation. We, further, demonstrate the change of p53 and GSK3 interaction rate, due to hypothetical catalytic reaction or external stimuli, can able to regulate the dynamics of the two network modules, and even can control their dynamics to protect the system from cycle arrest (apoptosis).
      Graphical abstract image Highlights

      PubDate: 2015-08-24T19:45:31Z
       
  • Crosstalk events in the estrogen signaling pathway may affect tamoxifen
           efficacy in breast cancer molecular subtypes
    • Abstract: Publication date: Available online 19 August 2015
      Source:Computational Biology and Chemistry
      Author(s): Guillermo de Anda-Jáuregui, Raúl A. Mejía-Pedroza, Jesús Espinal-Enríquez, Enrique Hernández-Lemus
      Steroid hormones are involved on cell growth, development and differentiation. Such effects are often mediated by steroid receptors. One paradigmatic example of this coupling is the estrogen signaling pathway. Its dysregulation is involved in most tumors of the mammary gland. It is thus an important pharmacological target in breast cancer. This pathway, however, crosstalks with several other molecular pathways, a fact that may have consequences for the effectiveness of hormone modulating drug therapies, such as tamoxifen. For this work, we performed a systematic analysis of the major routes involved in crosstalk phenomena with the estrogen pathway –based on gene expression experiments (819 samples) and pathway analysis (493 samples)– for biopsy-captured tissue and contrasted in two independent datasets with in vivo and in vitro pharmacological stimulation. Our results confirm the presence of a number of crosstalk events across the estrogen signaling pathway with others that are dysregulated in different molecular subtypes of breast cancer. These may be involved in proliferation, invasiveness and apoptosis-evasion in patients. The results presented may open the way to new designs of adjuvant and neoadjuvant therapies for breast cancer treatment.


      PubDate: 2015-08-20T19:31:48Z
       
  • Reprint of “Abstraction for data integration: Fusing mammalian
           molecular, cellular and phenotype big datasets for better knowledge
           extraction”
    • Abstract: Publication date: Available online 18 August 2015
      Source:Computational Biology and Chemistry
      Author(s): Andrew D. Rouillard, Zichen Wang, Avi Ma’ayan
      With advances in genomics, transcriptomics, metabolomics and proteomics, and more expansive electronic clinical record monitoring, as well as advances in computation, we have entered the Big Data era in biomedical research. Data gathering is growing rapidly while only a small fraction of this data is converted to useful knowledge or reused in future studies. To improve this, an important concept that is often overlooked is data abstraction. To fuse and reuse biomedical datasets from diverse resources, data abstraction is frequently required. Here we summarize some of the major Big Data biomedical research resources for genomics, proteomics and phenotype data, collected from mammalian cells, tissues and organisms. We then suggest simple data abstraction methods for fusing this diverse but related data. Finally, we demonstrate examples of the potential utility of such data integration efforts, while warning about the inherit biases that exist within such data.
      Graphical abstract image

      PubDate: 2015-08-20T19:31:48Z
       
  • Molecular Docking Study of Natural Alkaloids as Multi-targeted Hedgehog
           Pathway Inhibitors in Cancer Stem Cell Therapy
    • Abstract: Publication date: Available online 6 August 2015
      Source:Computational Biology and Chemistry
      Author(s): Mayank, Vikas Jaitak
      Cancer is responsible for millions of deaths throughout the world every year. Increased understanding as well as advancements in the therapeutic aspect seems suboptimal to restrict the huge deaths associated with cancer. The major cause responsible for this is high resistance as well as relapse rate associated with cancers. Several evidences indicated that cancer stem cells (CSC) are mainly responsible for the resistance and relapses associated with cancer. Furthermore, agents targeting a single protein seem to have higher chances of resistance than multitargeting drugs. According to the concept of network model, partial inhibition of multiple targets is more productive than single hit agents. Thus, by fusing both the premises that CSC and single hit anticancer drugs, both are responsible for cancer related resistances and screened alkaloids for the search of leads having CSC targeting ability as well as the capability to modulating multiple target proteins. The in silico experimental data indicated that emetine and cortistatin have the ability to modulate hedgehog (Hh) pathway by binding to sonic hedgehog (Hh), smoothened (Smo) and Gli protein, involved in maintenance CSCs. Furthermore, solamargine, solasonine and tylophorine are also seems to be good lead molecules targeting towards CSCs by modulating Hh pathway. Except solamargine and solasonine, other best lead molecules also showed acceptable in silico ADME profile. The predicted lead molecules can be suitably modified to get multitargeting CSC targeting agent to get rid of associate resistances.


      PubDate: 2015-08-08T17:45:25Z
       
  • H7N9 Influenza Outbreak in China 2013: In silico Analyses of Conserved
           Segments of the Hemagglutinin as a basis for the Selection of Peptide
           Vaccine Targets
    • Abstract: Publication date: Available online 7 August 2015
      Source:Computational Biology and Chemistry
      Author(s): Tapati Sarkar, Sukhen Das, Antara De, Papiya Nandy, Shiladitya Chattopadhyay, Mamta Chawla-Sarkar, Ashesh Nandy
      The sudden emergence of a human infecting strain of H7N9 influenza virus in China in 2013 leading to fatalities in about 30% of the cases has caused wide concern that additional mutations in the strain leading to human to human transmission could lead to a deadly pandemic. It may happen in a short time span as the outbreak of H7N9 is more and more recurrent, which implies that H7N9 evolution is speeding up. H7N9 flu strains were not known to infect humans before this attack in China in February 2013 and it was solely an avian strain. While currently available drugs such as oseltamivir have been found to be largely effective against the H7N9, albeit with recent reported cases of development of resistance to the drug, there is a necessity to identify alternatives to combat this disease, especially if it assumes pandemic proportions. In our work, we have tried to investigate for the genetic changes in hemagglutinin (HA) protein sequence that lead to human infection by an avian infecting virus and identify possible peptide targets to design vaccines to control this upcoming risk. We identified three highly conserved regions in all H7 subtypes, of which one particular immunogenic surface exposed region was found to be well conserved in all human infecting H7N9 strains (accessed up to 27th March 2014). Compared to H7N9 avian strains, we identified two mutations in this conserved region at the receptor binding site of all post-February 2013 human-infecting H7N9China hemagglutinin protein sequences. One of the mutations is very close (3.6A°) to the hemagglutinin sialic acid binding pocket that may lead to better binding to human host's sialic acid due to the changes in hydrophobicity of the microenvironment of the binding site. We found that the peptide region with these mutational changes that are specific for human infecting H7N9 virus possess the possibility of being used as target for a peptide vaccine.
      Graphical abstract image

      PubDate: 2015-08-08T17:45:25Z
       
  • Enzyme function is regulated by its localization.
    • Abstract: Publication date: Available online 8 August 2015
      Source:Computational Biology and Chemistry
      Author(s): Stacey M. Gifford, Pablo Meyer
      To better understand how enzyme localization affects enzyme activity we studied the cellular localization of the glycosyltransferase MurG, an enzyme necessary for cell wall synthesis at the spore during sporulation in the bacterium B.subtilis. During sporulation MurG was gradually enriched to the membrane at the forespore and point mutations in a MurG helical domain disrupting its localization to the membrane caused severe sporulation defects, but did not affect localization nor caused detectable defects during exponential growth. We found that this localization is dependent on the phospholipid cardiolipin, as in strains where the cardiolipin-synthesizing genes were deleted, MurG levels were diminished at the forespore. Furthermore, in this cardiolipin-less strain, MurG localization during sporulation was rescued by external addition of purified cardiolipin. These results support localization as a critical factor in the regulation of proper enzyme function and catalysis.
      Graphical abstract image

      PubDate: 2015-08-08T17:45:25Z
       
  • Genome-wide identification of galactinol synthase (GolS) genes in Solanum
           lycopersicum and Brachypodium distachyon
    • Abstract: Publication date: October 2015
      Source:Computational Biology and Chemistry, Volume 58
      Author(s): Ertugrul Filiz, Ibrahim Ilker Ozyigit, Recep Vatansever
      GolS genes stand as potential candidate genes for molecular breeding and/or engineering programs in order for improving abiotic stress tolerance in plant species. In this study, a total of six galactinol synthase (GolS) genes/proteins were retrieved for Solanum lycopersicum and Brachypodium distachyon. GolS protein sequences were identified to include glyco_transf_8 (PF01501) domain structure, and to have a close molecular weight (36.40–39.59kDa) and amino acid length (318–347 aa) with a slightly acidic pI (5.35–6.40). The sub-cellular location was mainly predicted as cytoplasmic. S. lycopersicum genes located on chr 1 and 2, and included one segmental duplication while genes of B. distachyon were only on chr 1 with one tandem duplication. GolS sequences were found to have well conserved motif structures. Cis-acting analysis was performed for three abiotic stress responsive elements, including ABA responsive element (ABRE), dehydration and cold responsive elements (DRE/CRT) and low-temperature responsive element (LTRE). ABRE elements were found in all GolS genes, except for SlGolS4; DRE/CRT was not detected in any GolS genes and LTRE element found in SlGolS1 and BdGolS1 genes. AU analysis in UTR and ORF regions indicated that SlGolS and BdGolS mRNAs may have a short half-life. SlGolS3 and SlGolS4 genes may generate more stable transcripts since they included AATTAAA motif for polyadenylation signal POLASIG2. Seconder structures of SlGolS proteins were well conserved than that of BdGolS. Some structural divergences were detected in 3D structures and predicted binding sites exhibited various patterns in GolS proteins.


      PubDate: 2015-07-31T21:00:34Z
       
  • Distribution of putative xenogeneic silencers in prokaryote genomes
    • Abstract: Publication date: Available online 23 July 2015
      Source:Computational Biology and Chemistry
      Author(s): Ernesto Perez-Rueda, J.Antonio Ibarra
      Gene silencing is an important function as it keeps newly acquired foreign DNA repressed, thereby avoiding possible deleterious effects in the host organism. Known transcriptional regulators associated with this process are called xenogeneic silencers (XS) and belong to either the H-NS, Lsr2, MvaT or Rok families. In the work described here we looked for XS-like regulators and their distribution in prokaryotic organisms was evaluated. Our analysis showed that putative XS regulators similar to H-NS, Lsr2, MvaT or Rok are present only in bacteria (31.7 %). This does not exclude the existence of alternative XS in the rest of the organisms analyzed. Additionally, of the four XS groups evaluated in this work, those from the H-NS family have diversified more than the other groups. In order to compare the distribution of these putative XS regulators we also searched for other nucleoid-associated proteins (NAPs) not included in this group such as Fis, EbfC/YbaB, HU/IHF and Alba. Results showed that NAPs from the Fis, EbfC/YbaB, HU/IHF and Alba families are widely (94 %) distributed among prokaryotes. These NAPs were found in multiple combinations with or without XS-like proteins. In regard with XS regulators, results showed that only XS proteins from one family were found in those organisms containing them. This suggests specificity for this type of regulators and their corresponding genomes.
      Graphical abstract image

      PubDate: 2015-07-31T21:00:34Z
       
  • Theoretical investigations on the interactions of glucokinase regulatory
           protein with fructose phosphates
    • Abstract: Publication date: Available online 29 July 2015
      Source:Computational Biology and Chemistry
      Author(s): Baoping Ling, Xueyuan Yan, Min Sun, Siwei Bi
      Glucokinase (GK) plays a critical role in maintaining glucose homeostasis in the human liver and pancreas. In the liver, the activity of GK is modulated by the glucokinase regulatory protein (GKRP) which functions as a competitive inhibitor of glucose to bind to GK. Moreover, the inhibitory intensity of GKRP to GK is suppressed by fructose 1-phosphate (F1P), and reinforced by fructose 6-phosphate (F6P). Here, we employed a series of computational techniques to explore the interactions of fructose phosphates with GKRP. Calculation results reveal that F1P and F6P can bind to the same active site of GKRP with different binding modes, and electrostatic interaction provides a major driving force for the ligand binding. The presence of fructose phosphate severely influences the motions of protein and the conformational space, and the structural change of sugar phosphate influences its interactions with GKRP, leading to a large conformational rearrangement of loop2 in the SIS2 domain. In particular, the binding of F6P to GKRP facilitates the protruding loop2 contacting with GK to form the stable GK-GKRP complex. The conserved residues 179-184 of GKRP play a major role in the binding of phosphate group and maintaining the stability of GKRP. These results may provide deep insight into the regulatory mechanism of GKRP to the activity of GK.
      Graphical abstract image

      PubDate: 2015-07-31T21:00:34Z
       
  • An efficient approach for the prediction of ion channels and their
           subfamilies
    • Abstract: Publication date: Available online 26 July 2015
      Source:Computational Biology and Chemistry
      Author(s): Arvind Kumar Tiwari, Rajeev Srivastava
      Ion channel are integral membrane protein that are responsible for controlling the flow of ions across the cell. There are various biological functions that are performed by different types of ion channels. Therefore for new drug discovery it is necessary to develop a novel computational intelligence techniques based approach for the reliable prediction of ion channels families and their subfamilies. In this paper random forest based approach is proposed to predict ion channels families and their subfamilies by using sequence derived features. Here, seven feature vectors are used to represent the protein sample, including amino acid composition, dipeptide composition, correlation features, composition, transition, distribution and pseudo amino acid composition. The minimum redundancy and maximum relevance feature selection is used to find the optimal number of features for improving the prediction performance. The proposed method achieved an overall accuracy of 100%, 98.01%, 91.5%, 93.0%, 92.2%, 78.6%, 95.5%, 84.9%, MCC values of 1.00, 0.92, 0.88, 0.88, 0.90, 0.79, 0.91, 0.81 and ROC area values of 1.00, 0.99, 0.99, 0.99, 0.99, 0.95, 0.99 and 0.96 using 10-fold cross validation to predict the ion channels and non-ion channels, voltage gated ion channels and ligand gated ion channels, four subfamilies (calcium, potassium, sodium and chloride) of voltage gated ion channels, and four subfamilies of ligand gated ion channels and predict subfamilies of voltage gated calcium, potassium, sodium and chloride ion channels respectively.
      Graphical abstract image

      PubDate: 2015-07-31T21:00:34Z
       
  • Structural and energetic insight into the interactions between the
           benzolactam inhibitors and tumor marker HSP90α
    • Abstract: Publication date: Available online 29 July 2015
      Source:Computational Biology and Chemistry
      Author(s): Xiao-Yan Guo, Run-Peng Qi, De-Gang Xu, Xu-Hua Liu, Xiao Yang
      The heat shock protein 90α (HSP90α) provides a promising molecular target for cancer therapy. A series of novel benzolactam inhibitors exhibited distinct inhibitory activity for HSP90α. However, the structural basis for the impact of distinct R1 substituent groups of nine benzolactam inhibitors on HSP90α binding affinities remains unknown. In this study, we carried out molecular docking, molecular dynamics (MD) simulations, and molecular mechanics and generalized Born/surface area (MM-GBSA) binding free energy calculations to address the differences. Molecular docking studies indicated that all nine compounds presented one conformation in the ATP-binding site of HSP90α N-terminal domain. MD simulations and subsequent MM-GBSA calculations revealed that the hydrophobic interactions between all compounds and HSP90α contributed the most to the binding affinity and a good linear correlation was obtained between the calculated and the experimental binding free energies (R = 0.88). The per residue decomposition revealed that the most remarkable differences of residue contributions were found in the residues Ala55, Ile96, and Leu107 defining a hydrophobic pocket for the R1 group, consistent with the analysis of binding modes. This study may be helpful for the future design of novel HSP90α inhibitors.
      Graphical abstract image

      PubDate: 2015-07-31T21:00:34Z
       
  • Cavities create a potential back door in epoxide hydrolase Rv1938 from
           Mycobacterium tuberculosis—A molecular dynamics simulation study
    • Abstract: Publication date: Available online 29 July 2015
      Source:Computational Biology and Chemistry
      Author(s): Anitha Selvan, Sharmila Anishetty
      Mycobacterium tuberculosis (Mtb) is the causative organism of tuberculosis. Extensively drug resistant strains and latency have posed formidable challenges in the treatment of tuberculosis. The current study addresses an alpha/beta hydrolase fold bearing enzyme, epoxide hydrolase Rv1938 from Mtb. Epoxide hydrolases are involved in detoxification processes, catabolism and regulation of signaling molecules. Using GROMACS, a 100ns Molecular dynamics (MD) simulation was performed for Rv1938. Cavities were identified within the protein at various time frames of the simulation and their volumes were computed. During MD simulation, in addition to the substrate binding cavity, opening of two new cavities located behind the active site was observed. These cavities may be similar to the backdoor proposed for acetylcholinesterase. Structural superimposition of epoxide hydrolase from Mtb with the epoxide hydrolase of Agrobacterium radiobacter1 AD1 (Ephy) indicates that cavity1 in Mtb lies at an identical position to that of the water tunnel in Ephy. Further, docking of the substrate and an inhibitor with protein structures obtained from MD simulation at various time frames was also performed. The potential role of these cavities is discussed.
      Graphical abstract image

      PubDate: 2015-07-31T21:00:34Z
       
  • MATEPRED—A svm-based prediction method for multidrug and toxin
           extrusion (MATE) proteins
    • Abstract: Publication date: Available online 29 July 2015
      Source:Computational Biology and Chemistry
      Author(s): Tamanna, Jayashree Ramana
      The growth and spread of drug resistance in bacteria have been well established in both mankind and beasts and thus is a serious public health concern. Due to the increasing problem of drug resistance, control of infectious diseases like diarrhea, pneumonia etc. is becoming more difficult. Hence, it is crucial to understand the underlying mechanism of drug resistance mechanism and devising novel solution to address this problem. Multidrug And Toxin Extrusion (MATE) proteins, first characterized as bacterial drug transporters, are present in almost all species. It plays a very important function in the secretion of cationic drugs across the cell membrane. In this work, we propose SVM based method for prediction of MATE proteins. The data set employed for training consists of 189 non-redundant protein sequences, that are further classified as positive (63 sequences) set comprising of sequences from MATE family, and negative (126 sequences) set having protein sequences from other transporters families proteins and random protein sequences taken from NCBI while in the test set, there are 120 protein sequences in all (8 in positive and 112 in negative set). The model was derived using Position Specific Scoring Matrix (PSSM) composition and achieved an overall accuracy 92.06%. The five-fold cross validation was used to optimize SVM parameter and select the best model. The prediction algorithm presented here is implemented as a freely available web server MATEpred, which will assist in rapid identification of MATE proteins.
      Graphical abstract image

      PubDate: 2015-07-31T21:00:34Z
       
  • CAMWI: Detecting Protein Complexes Using Weighted Clustering Coefficient
           and Weighted Density
    • Abstract: Publication date: Available online 30 July 2015
      Source:Computational Biology and Chemistry
      Author(s): Amir lakizadeh, Saeed Jalili, Sayed-Amir Marashi
      Detection of protein complexes is very important to understand the principles of cellular organization and function. Recently, large Protein-Protein Interactions (PPIs) networks have become available using high-throughput experimental techniques. These networks make it possible to develop computational methods for protein complex detection. Most of the current methods rely on the assumption that protein complex as a module has dense structure. However complexes have core-attachment structure and proteins in a complex core share a high degree of functional similarity, so it expects that a core has high weighted density. In this paper we present a Core-Attachment based method for protein complex detection from Weighted PPI Interactions using clustering coefficient and weighted density. Experimental results show that the proposed method, CAMWI improves the accuracy of protein complex detection.


      PubDate: 2015-07-31T21:00:34Z
       
  • A combined systems and structural modeling approach repositions
           antibiotics for Mycoplasma genitalium
    • Abstract: Publication date: Available online 30 July 2015
      Source:Computational Biology and Chemistry
      Author(s): Denis Kazakiewicz, Jonathan R. Karr, Karol M. Langner, Dariusz Plewczynski
      Bacteria are increasingly resistant to existing antibiotics, which target a narrow range of pathways. New methods are needed to identify targets, including repositioning targets among distantly related species. We developed a novel combination of systems and structural modeling and bioinformatics to reposition known antibiotics and targets to new species. We applied this approach to Mycoplasma genitalium, a common cause of urethritis. First, we used quantitative metabolic modeling to identify enzymes whose expression affects the cellular growth rate. Second, we searched the literature for inhibitors of homologs of the most fragile enzymes. Next, we used sequence alignment to assess that the binding site is shared by M. genitalium, but not by humans. Lastly, we used molecular docking to verify that the reported inhibitors preferentially interact with M. genitalium proteins over their human homologs. Thymidylate kinase was the top predicted target and piperidinylthymines were the top compounds. Further work is needed to experimentally validate piperidinylthymines. In summary, combined systems and structural modeling is a powerful tool for drug repositioning.
      Graphical abstract image

      PubDate: 2015-07-31T21:00:34Z
       
  • Machine Learnable Fold Space Representation based on Residue Cluster
           Classes
    • Abstract: Publication date: Available online 30 July 2015
      Source:Computational Biology and Chemistry
      Author(s): Ricardo Corral-Corral, Edgar Chavez, Gabriel Del Rio
      Motivation Protein fold space is a conceptual framework where all possible protein folds exist and ideas about protein structure, function and evolution may be analyzed. Classification of protein folds in this space is commonly achieved by using similarity indexes and/or machine learning approaches, each with different limitations. Results We propose a method for constructing a compact vector space model of protein fold space by representing each protein structure by its residues local contacts. We developed an efficient method to statistically test for the separability of points in a space and showed that our protein fold space representation is learnable by any machine-learning algorithm. Availability An API is freely available at https://code.google.com/p/pyrcc/.
      Graphical abstract image Highlights

      PubDate: 2015-07-31T21:00:34Z
       
  • Network-based ranking methods for prediction of novel disease associated
           microRNAs
    • Abstract: Publication date: October 2015
      Source:Computational Biology and Chemistry, Volume 58
      Author(s): Duc-Hau Le
      Background Many studies have shown roles of microRNAs on human disease and a number of computational methods have been proposed to predict such associations by ranking candidate microRNAs according to their relevance to a disease. Among them, machine learning-based methods usually have a limitation in specifying non-disease microRNAs as negative training samples. Meanwhile, network-based methods are becoming dominant since they well exploit a “disease module” principle in microRNA functional similarity networks. Of which, random walk with restart (RWR) algorithm-based method is currently state-of-the-art. The use of this algorithm was inspired from its success in predicting disease gene because the “disease module” principle also exists in protein interaction networks. Besides, many algorithms designed for webpage ranking have been successfully applied in ranking disease candidate genes because web networks share topological properties with protein interaction networks. However, these algorithms have not yet been utilized for disease microRNA prediction. Methods We constructed microRNA functional similarity networks based on shared targets of microRNAs, and then we integrated them with a microRNA functional synergistic network, which was recently identified. After analyzing topological properties of these networks, in addition to RWR, we assessed the performance of (i) PRINCE (PRIoritizatioN and Complex Elucidation), which was proposed for disease gene prediction; (ii) PageRank with Priors (PRP) and K-Step Markov (KSM), which were used for studying web networks; and (iii) a neighborhood-based algorithm. Results Analyses on topological properties showed that all microRNA functional similarity networks are small-worldness and scale-free. The performance of each algorithm was assessed based on average AUC values on 35 disease phenotypes and average rankings of newly discovered disease microRNAs. As a result, the performance on the integrated network was better than that on individual ones. In addition, the performance of PRINCE, PRP and KSM was comparable with that of RWR, whereas it was worst for the neighborhood-based algorithm. Moreover, all the algorithms were stable with the change of parameters. Final, using the integrated network, we predicted six novel miRNAs (i.e., hsa-miR-101, hsa-miR-181d, hsa-miR-192, hsa-miR-423-3p, hsa-miR-484 and hsa-miR-98) associated with breast cancer. Conclusions Network-based ranking algorithms, which were successfully applied for either disease gene prediction or for studying social/web networks, can be also used effectively for disease microRNA prediction.
      Graphical abstract image

      PubDate: 2015-07-31T21:00:34Z
       
  • Analysis of image-based phenotypic parameters for high throughput gene
           perturbation assays
    • Abstract: Publication date: Available online 19 July 2015
      Source:Computational Biology and Chemistry
      Author(s): Mee Song, Euna Jeong, Tae-Kyu Lee, Yury Tsoy, Yong-Jun Kwon, Sukjoon Yoon
      Although image-based phenotypic assays are considered a powerful tool for siRNA library screening, the reproducibility and biological implications of various image-based assays are not well-characterized in a systematic manner. Here, we compared the resolution of high throughput assays of image-based cell count and typical cell viability measures for cancer samples. It was found that the optimal plating density of cells was important to obtain maximal resolution in both types of assays. In general, cell counting provided better resolution than the cell viability measure in diverse batches of siRNAs. In addition to cell count, diverse image-based measures were simultaneously collected from a single screening and showed good reproducibility in repetitions. They were classified into a few functional categories according to biological process, based on the differential patterns of hit (i.e., siRNAs) prioritization from the same screening data. The presented systematic analyses of image-based parameters provide new insight to a multitude of applications and better biological interpretation of high content cell-based assays.
      Graphical abstract image

      PubDate: 2015-07-19T21:39:34Z
       
  • MOEPGA: A Novel Method to Detect Protein Complexes in Yeast
           Protein–Protein Interaction Networks based on MultiObjective
           Evolutionary Programming Genetic Algorithm
    • Abstract: Publication date: Available online 7 July 2015
      Source:Computational Biology and Chemistry
      Author(s): Buwen Cao , Jiawei Luo , Cheng Liang , Shulin Wang
      The identification of protein complexes in Protein-Protein Interaction (PPI) networks has greatly advanced our understanding of biological organisms. Existing computational methods to detect protein complexes are usually based on specific network topological properties of PPI networks. However, due to the inherent complexity of the network structures, the identification of protein complexes may not be fully addressed by using single network topological property. In this study, we propose a novel MultiObjective Evolutionary Programming Genetic Algorithm (MOEPGA) which integrates multiple network topological features to detect biologically meaningful protein complexes. Our approach first systematically analyzes the multiobjective problem in terms of identifying protein complexes from PPI networks, and then constructs the objective function of the iterative algorithm based on three common topological properties of protein complexes from the benchmark dataset, finally we describe our algorithm, which mainly consists of three steps, population initialization, subgraph mutation and subgraph selection operation. To show the utility of our method, we compared MOEPGA with several state-of-the-art algorithms on two yeast PPI datasets. The experiment results demonstrate that the proposed method can not only find more protein complexes but also achieve higher accuracy in terms of fscore. Moreover, our approach can cover a certain number of proteins in the input PPI network in terms of the normalized clustering score. Taken together, our method can serve as a powerful framework to detect protein complexes in yeast PPI networks, thereby facilitating the identification of the underlying biological functions.
      Graphical abstract image

      PubDate: 2015-07-11T15:49:22Z
       
  • Comparative analysis of plant lycopene cyclases
    • Abstract: Publication date: October 2015
      Source:Computational Biology and Chemistry, Volume 58
      Author(s): Ibrahim Koc , Ertugrul Filiz , Huseyin Tombuloglu
      Carotenoids are essential isoprenoid pigments produced by plants, algae, fungi and bacteria. Lycopene cyclase (LYC) commonly cyclize carotenoids, which is an important branching step in the carotenogenesis, at one or both end of the backbone. Plants have two types of LYC (β-LCY and ϵ-LCY). In this study, plant LYCs were analyzed. Based on domain analysis, all LYCs accommodate lycopene cyclase domain (Pf05834). Furthermore, motif analysis indicated that motifs were conserved among the plants. On the basis of phylogenetic analysis, β-LCYs and ϵ-LCYs were classified in β and ϵ groups. Monocot and dicot plants separated from each other in the phylogenetic tree. Subsequently, Oryza sativa Japonica Group and Zea mays of LYCs as monocot plants and Vitis vinifera and Solanum lycopersicum of LYCs as dicot plants were analyzed. According to nucleotide diversity analysis of β-LCY and ϵ-LCY genes, nucleotide diversities were found to be π: 0.30 and π: 0.25, respectively. The result highlighted β-LCY genes showed higher nucleotide diversity than ϵ-LCY genes. LYCs interacting genes and their co-expression partners were also predicted using String server. The obtained data suggested the importance of LYCs in carotenoid metabolism. 3D modeling revealed that depicted structures were similar in O. sativa, Z mays, S. lycopersicum, and V. vinifera β-LCYs and ϵ-LCYs. Likewise, the predicted binding sites were highly similar between O. sativa, Z mays, S. lycopersicum, and V. vinifera LCYs. Most importantly, analysis elucidated the V/IXGXGXXGXXXA motif for both type of LYC (β-LCY and ϵ-LCY). This motif related to Rossmann fold domain and probably provides a flat platform for binding of FAD in O. sativa, Z mays, S. lycopersicum, and V. vinifera β-LCYs and ϵ-LCYs with conserved structure. In addition to lycopene cyclase domain, the V/IXGXGXXGXXXA motif can be used for exploring LYCs proteins and to annotate the function of unknown proteins containing lycopene cyclase domain. Overall results indicated that a high degree of conserved signature were observed in plant LYCs.
      Graphical abstract image

      PubDate: 2015-07-03T15:42:05Z
       
  • Abstraction for data integration: Fusing mammalian molecular, cellular and
           phenotype big datasets for better knowledge extraction
    • Abstract: Publication date: October 2015
      Source:Computational Biology and Chemistry, Volume 58
      Author(s): Andrew D. Rouillard , Zichen Wang , Avi Ma’ayan
      With advances in genomics, transcriptomics, metabolomics and proteomics, and more expansive electronic clinical record monitoring, as well as advances in computation, we have entered the Big Data era in biomedical research. Data gathering is growing rapidly while only a small fraction of this data is converted to useful knowledge or reused in future studies. To improve this, an important concept that is often overlooked is data abstraction. To fuse and reuse biomedical datasets from diverse resources, data abstraction is frequently required. Here we summarize some of the major Big Data biomedical research resources for genomics, proteomics and phenotype data, collected from mammalian cells, tissues and organisms. We then suggest simple data abstraction methods for fusing this diverse but related data. Finally, we demonstrate examples of the potential utility of such data integration efforts, while warning about the inherit biases that exist within such data.
      Graphical abstract image

      PubDate: 2015-07-03T15:42:05Z
       
  • Identification of evolutionarily conserved Momordica charantia microRNAs
           using computational approach and its utility in phylogeny analysis
    • Abstract: Publication date: October 2015
      Source:Computational Biology and Chemistry, Volume 58
      Author(s): Krishnaraj Thirugnanasambantham , Subramanian Saravanan , Kulandaivelu Karikalan , Rajaraman Bharanidharan , Perumal Lalitha , S. Ilango , Villianur Ibrahim HairulIslam
      Momordica charantia (bitter gourd, bitter melon) is a monoecious Cucurbitaceae with anti-oxidant, anti-microbial, anti-viral and anti-diabetic potential. Molecular studies on this economically valuable plant are very essential to understand its phylogeny and evolution. MicroRNAs (miRNAs) are conserved, small, non-coding RNA with ability to regulate gene expression by bind the 3′ UTR region of target mRNA and are evolved at different rates in different plant species. In this study we have utilized homology based computational approach and identified 27 mature miRNAs for the first time from this bio-medically important plant. The phylogenetic tree developed from binary data derived from the data on presence/absence of the identified miRNAs were noticed to be uncertain and biased. Most of the identified miRNAs were highly conserved among the plant species and sequence based phylogeny analysis of miRNAs resolved the above difficulties in phylogeny approach using miRNA. Predicted gene targets of the identified miRNAs revealed their importance in regulation of plant developmental process. Reported miRNAs held sequence conservation in mature miRNAs and the detailed phylogeny analysis of pre-miRNA sequences revealed genus specific segregation of clusters.
      Graphical abstract image

      PubDate: 2015-07-03T15:42:05Z
       
  • Construction of regulatory networks mediated by small RNAs responsive to
           abiotic stresses in rice (Oryza sativa)
    • Abstract: Publication date: October 2015
      Source:Computational Biology and Chemistry, Volume 58
      Author(s): Jingping Qin , Xiaoxia Ma , Zhonghai Tang , Yijun Meng
      Plants have evolved exquisite molecular mechanisms to adapt to diverse abiotic stresses. MicroRNAs play an important role in stress response in plants. However, whether the other small RNAs (sRNAs) possess stress-related roles remains elusive. In this study, thousands of sRNAs responsive to cold, drought and salt stresses were identified in rice seedlings and panicles by using high-throughput sequencing data. These sRNAs were classified into 12 categories, including “Panicle_Cold_Down”, “Panicle_Cold_Up”, “Panicle_Drought_Down”, “Panicle_Drought_Up”, “Panicle_Salt_Down”, “Panicle_Salt_Up”, “Seedling_Cold_Down”, “Seedling_Cold_Up”, “Seedling_Drought_Down”, “Seedling_Drought_Up”, “Seedling_Salt_Down” and “Seedling_Salt_Up”. The stress-responsive sRNAs enriched in Argonaute 1 were extracted for target prediction and degradome sequencing data-based validation, which enabled network construction. Within certain subnetworks, some target genes were further supported by microarray data. Literature mining indicated that certain targets were potentially involved in stress response. These results demonstrate that the established networks are biologically meaningful. We discovered that in some cases, one sRNA sequence could be assigned to two or more categories. Moreover, within certain target-centered subnetworks, one transcript was regulated by several stress-responsive sRNAs assigned to different categories. It implies that these subnetworks are potentially implicated in stress signal crosstalk. Together, our results could advance the current understanding of the biological role of plant sRNAs in stress signaling.
      Graphical abstract image

      PubDate: 2015-07-03T15:42:05Z
       
  • Title page
    • Abstract: Publication date: August 2015
      Source:Computational Biology and Chemistry, Volume 57




      PubDate: 2015-07-03T15:42:05Z
       
  • No title
    • Abstract: Publication date: August 2015
      Source:Computational Biology and Chemistry, Volume 57
      Author(s): Hsien-Da Huang , Yi-Ping Phoebe Chen



      PubDate: 2015-07-03T15:42:05Z
       
  • CUDA ClustalW: An efficient parallel algorithm for progressive multiple
           sequence alignment on Multi-GPUs
    • Abstract: Publication date: October 2015
      Source:Computational Biology and Chemistry, Volume 58
      Author(s): Che-Lun Hung , Yu-Shiang Lin , Chun-Yuan Lin , Yeh-Ching Chung , Yi-Fang Chung
      For biological applications, sequence alignment is an important strategy to analyze DNA and protein sequences. Multiple sequence alignment is an essential methodology to study biological data, such as homology modeling, phylogenetic reconstruction and etc. However, multiple sequence alignment is a NP-hard problem. In the past decades, progressive approach has been proposed to successfully align multiple sequences by adopting iterative pairwise alignments. Due to rapid growth of the next generation sequencing technologies, a large number of sequences can be produced in a short period of time. When the problem instance is large, progressive alignment will be time consuming. Parallel computing is a suitable solution for such applications, and GPU is one of the important architectures for contemporary parallel computing researches. Therefore, we proposed a GPU version of ClustalW v2.0.11, called CUDA ClustalW v1.0, in this work. From the experiment results, it can be seen that the CUDA ClustalW v1.0 can achieve more than 33× speedups for overall execution time by comparing to ClustalW v2.0.11.
      Graphical abstract image

      PubDate: 2015-07-03T15:42:05Z
       
  • IFC Editorial Board
    • Abstract: Publication date: August 2015
      Source:Computational Biology and Chemistry, Volume 57




      PubDate: 2015-07-03T15:42:05Z
       
  • The aspartate aminotransferase-like domain of Firmicutes MocR
           transcriptional regulators
    • Abstract: Publication date: October 2015
      Source:Computational Biology and Chemistry, Volume 58
      Author(s): Teresa Milano , Roberto Contestabile , Alessandra Lo Presti , Massimo Ciccozzi , Stefano Pascarella
      Bacterial MocR transcriptional regulators possess an N-terminal DNA-binding domain containing a conserved helix-turn-helix module and an effector-binding and/or oligomerization domain at the C-terminus, homologous to fold type-I pyridoxal 5′-phosphate (PLP) enzymes. Since a comprehensive structural analysis of the MocR regulators is still missing, a comparisons of Firmicutes MocR sequences was undertook to contribute to the understanding of the structural characteristics of the C-terminal domain of these proteins, and to shed light on the structural and functional relationship with fold type-I PLP enzymes. Results of this work suggest the presence of at least three subgroups within the MocR sequences and provide a guide for rational site-directed mutagenesis studies aimed at deciphering the structure-function relationships in this new protein family.
      Graphical abstract image

      PubDate: 2015-07-03T15:42:05Z
       
  • Computational prediction and biochemical characterization of novel RNA
           aptamers to Rift Valley fever virus nucleocapsid protein
    • Abstract: Publication date: October 2015
      Source:Computational Biology and Chemistry, Volume 58
      Author(s): Mary Ellenbecker , Jeremy St. Goddard , Alec Sundet , Jean-Marc Lanchy , Douglas Raiford , J.Stephen Lodmell
      Rift Valley fever virus (RVFV) is a potent human and livestock pathogen endemic to sub-Saharan Africa and the Arabian Peninsula that has potential to spread to other parts of the world. Although there is no proven effective and safe treatment for RVFV infections, a potential therapeutic target is the virally encoded nucleocapsid protein (N). During the course of infection, N binds to viral RNA, and perturbation of this interaction can inhibit viral replication. To gain insight into how N recognizes viral RNA specifically, we designed an algorithm that uses a distance matrix and multidimensional scaling to compare the predicted secondary structures of known N-binding RNAs, or aptamers, that were isolated and characterized in previous in vitro evolution experiment. These aptamers did not exhibit overt sequence or predicted structure similarity, so we employed bioinformatic methods to propose novel aptamers based on analysis and clustering of secondary structures. We screened and scored the predicted secondary structures of novel randomly generated RNA sequences in silico and selected several of these putative N-binding RNAs whose secondary structures were similar to those of known N-binding RNAs. We found that overall the in silico generated RNA sequences bound well to N in vitro. Furthermore, introduction of these RNAs into cells prior to infection with RVFV inhibited viral replication in cell culture. This proof of concept study demonstrates how the predictive power of bioinformatics and the empirical power of biochemistry can be jointly harnessed to discover, synthesize, and test new RNA sequences that bind tightly to RVFV N protein. The approach would be easily generalizable to other applications.
      Graphical abstract image

      PubDate: 2015-07-03T15:42:05Z
       
  • Binding energies of tyrosine kinase inhibitors: Error assessment of
           computational methods for imatinib and nilotinib binding
    • Abstract: Publication date: October 2015
      Source:Computational Biology and Chemistry, Volume 58
      Author(s): Clifford W. Fong
      The binding energies of imatinib and nilotinib to tyrosine kinase have been determined by quantum mechanical (QM) computations, and compared with literature binding energy studies using molecular mechanics (MM). The potential errors in the computational methods include these critical factors: • Errors in X-ray structures such as structural distortions and steric clashes give unrealistically high van der Waals energies, and erroneous binding energies. • MM optimization gives a very different configuration to the QM optimization for nilotinib, whereas the imatinib ion gives similar configurations • Solvation energies are a major component of the overall binding energy. The QM based solvent model (PCM/SMD) gives different values from those used in the implicit PBSA solvent MM models. A major error in inhibitor—kinase binding lies in the non-polar solvation terms. • Solvent transfer free energies and the required empirical solvent accessible surface area factors for nilotinib and imatinib ion to give the transfer free energies have been reverse calculated. These values differ from those used in the MM PBSA studies. • An intertwined desolvation—conformational binding selectivity process is a balance of thermodynamic desolvation and intramolecular conformational kinetic control. • The configurational entropies (TΔS) are minor error sources.
      Graphical abstract image

      PubDate: 2015-07-03T15:42:05Z
       
  • Scaffold assembly based on genome rearrangement analysis
    • Abstract: Publication date: August 2015
      Source:Computational Biology and Chemistry, Volume 57
      Author(s): Sergey Aganezov , Nadia Sitdykova , Max A. Alekseyev
      Advances in DNA sequencing technology over the past decade have increased the volume of raw sequenced genomic data available for further assembly and analysis. While there exist many algorithms for assembly of sequenced genomic material, they often experience difficulties in constructing complete genomic sequences. Instead, they produce long genomic subsequences (scaffolds), which then become a subject to scaffold assembly aimed at reconstruction of their order along genome chromosomes. The balance between reliability and cost for scaffold assembly is not there just yet, which inspires one to seek for new approaches to address this problem. We present a new method for scaffold assembly based on the analysis of gene orders and genome rearrangements in multiple related genomes (some or even all of which may be fragmented). Evaluation of the proposed method on artificially fragmented mammalian genomes demonstrates its high reliability. We also apply our method for incomplete anophelinae genomes, which expose high fragmentation, and further validate the assembly results with referenced-based scaffolding. While the two methods demonstrate consistent results, the proposed method is able to identify more assembly points than the reference-based scaffolding.


      PubDate: 2015-07-03T15:42:05Z
       
  • Systems biology approach reveals possible evolutionarily conserved
           moonlighting functions for enolase
    • Abstract: Publication date: October 2015
      Source:Computational Biology and Chemistry, Volume 58
      Author(s): Gabriela Prado Paludo , Karina Rodrigues Lorenzatto , Diego Bonatto , Henrique Bunselmeyer Ferreira
      Glycolytic enzymes, such as enolase, have been described as multifunctional complex proteins that also display non-glycolytic activities, termed moonlighting functions. Although enolase multifunctionality has been described for several organisms, the conservation of enolase alternative functions through different phyla has not been explored with more details. A useful strategy to investigate moonlighting functions is the use of systems biology tools, which allow the prediction of protein functions/interactions by graph design and analysis. In this work, available information from protein–protein interaction (PPI) databases were used to design enolase PPI networks for four eukaryotic organisms, namely Homo sapiens, Drosophila melanogaster, Caenorhabditis elegans, and Saccharomyces cerevisiae, covering a wide spectrum of this domain of life. PPI networks with number of nodes ranging from 140 to 411 and up to 15,855 connections were generated, and modularity and centrality analyses, and functional enrichment were performed for all of them. The performed analyses showed that enolase is a central node within the networks, and that, in addition to its canonical interactions with proteins related to glycolysis and energetic metabolism, it is also part of protein clusters related to different biological processes, like transcription, development, and apoptosis, among others. Some of these non-glycolytic clusters, are partially conserved between networks, in terms of overall sharing of orthologs, overall cluster structure, and/or at the levels of key regulatory proteins within clusters. Overall, our results provided evidences of enolase multifunctionality and evolutionary conservation of enolase PPIs at all these levels.
      Graphical abstract image

      PubDate: 2015-07-03T15:42:05Z
       
  • The functional landscape bound to the transcription factors of Escherichia
           coli K-12
    • Abstract: Publication date: October 2015
      Source:Computational Biology and Chemistry, Volume 58
      Author(s): Ernesto Pérez-Rueda , Silvia Tenorio-Salgado , Alejandro Huerta-Saquero , Yalbi I. Balderas-Martínez , Gabriel Moreno-Hagelsieb
      Motivated by the experimental evidences accumulated in the last ten years and based on information deposited in RegulonDB, literature look up, and sequence analysis, we analyze the repertoire of 304 DNA-binding Transcription factors (TFs) in Escherichia coli K-12. These regulators were grouped in 78 evolutionary families and are regulating almost half of the total genes in this bacterium. In structural terms, 60% of TFs are composed by two-domains, 30% are monodomain, and 10% three- and four-structural domains. As previously noticed, the most abundant DNA-binding domain corresponds to the winged helix-turn-helix, with few alternative DNA-binding structures, resembling the hypothesis of successful protein structures with the emergence of new ones at low scales. In summary, we identified and described the characteristics associated to the DNA-binding TF in E. coli K-12. We also identified twelve functional modules based on a co-regulated gene matrix. Finally, diverse regulons were predicted based on direct associations between the TFs and potential regulated genes. This analysis should increase our knowledge about the gene regulation in the bacterium E. coli K-12, and provide more additional clues for comprehensive modelling of transcriptional regulatory networks in other bacteria.
      Graphical abstract image

      PubDate: 2015-07-03T15:42:05Z
       
  • GroupTracker: Video tracking system for multiple animals under severe
           occlusion
    • Abstract: Publication date: August 2015
      Source:Computational Biology and Chemistry, Volume 57
      Author(s): Tsukasa Fukunaga , Shoko Kubota , Shoji Oda , Wataru Iwasaki
      Quantitative analysis of behaviors shown by interacting multiple animals can provide a key for revealing high-order functions of their nervous systems. To resolve these complex behaviors, a video tracking system that preserves individual identity even under severe overlap in positions, i.e., occlusion, is needed. We developed GroupTracker, a multiple animal tracking system that accurately tracks individuals even under severe occlusion. As maximum likelihood estimation of Gaussian mixture model whose components can severely overlap is theoretically an ill-posed problem, we devised an expectation–maximization scheme with additional constraints on the eigenvalues of the covariance matrix of the mixture components. Our system was shown to accurately track multiple medaka (Oryzias latipes) which freely swim around in three dimensions and frequently overlap each other. As an accurate multiple animal tracking system, GroupTracker will contribute to revealing unexplored structures and patterns behind animal interactions. The Java source code of GroupTracker is available at https://sites.google.com/site/fukunagatsu/software/group-tracker.
      Graphical abstract image Highlights

      PubDate: 2015-07-03T15:42:05Z
       
  • Laplacian normalization and random walk on heterogeneous networks for
           disease-gene prioritization
    • Abstract: Publication date: August 2015
      Source:Computational Biology and Chemistry, Volume 57
      Author(s): Zhi-Qin Zhao , Guo-Sheng Han , Zu-Guo Yu , Jinyan Li
      Random walk on heterogeneous networks is a recently emerging approach to effective disease gene prioritization. Laplacian normalization is a technique capable of normalizing the weight of edges in a network. We use this technique to normalize the gene matrix and the phenotype matrix before the construction of the heterogeneous network, and also use this idea to define the transition matrices of the heterogeneous network. Our method has remarkably better performance than the existing methods for recovering known gene–phenotype relationships. The Shannon information entropy of the distribution of the transition probabilities in our networks is found to be smaller than the networks constructed by the existing methods, implying that a higher number of top-ranked genes can be verified as disease genes. In fact, the most probable gene–phenotype relationships ranked within top 3 or top 5 in our gene lists can be confirmed by the OMIM database for many cases. Our algorithms have shown remarkably superior performance over the state-of-the-art algorithms for recovering gene–phenotype relationships. All Matlab codes can be available upon email request.


      PubDate: 2015-07-03T15:42:05Z
       
  • Characterization and distribution of repetitive elements in association
           with genes in the human genome
    • Abstract: Publication date: August 2015
      Source:Computational Biology and Chemistry, Volume 57
      Author(s): Kai-Chiang Liang , Joseph T. Tseng , Shaw-Jenq Tsai , H. Sunny Sun
      Repetitive elements constitute more than 50% of the human genome. Recent studies implied that the complexity of living organisms is not just a direct outcome of a number of coding sequences; the repetitive elements, which do not encode proteins, may also play a significant role. Though scattered studies showed that repetitive elements in the regulatory regions of a gene control gene expression, no systematic survey has been done to report the characterization and distribution of various types of these repetitive elements in the human genome. Sequences from 5′ and 3′ untranslated regions and upstream and downstream of a gene were downloaded from the Ensembl database. The repetitive elements in the neighboring of each gene were identified and classified using cross-matching implemented in the RepeatMasker. The annotation and distribution of distinct classes of repetitive elements associated with individual gene were collected to characterize genes in association with different types of repetitive elements using systems biology program. We identified a total of 1,068,400 repetitive elements which belong to 37-class families and 1235 subclasses that are associated with 33,761 genes and 57,365 transcripts. In addition, we found that the tandem repeats preferentially locate proximal to the transcription start site (TSS) of genes and the major function of these genes are involved in developmental processes. On the other hand, interspersed repetitive elements showed a tendency to be accumulated at distal region from the TSS and the function of interspersed repeat-containing genes took part in the catabolic/metabolic processes. Results from the distribution analysis were collected and used to construct a gene-based repetitive element database (GBRED; http://www.binfo.ncku.edu.tw/GBRED/index.html). A user-friendly web interface was designed to provide the information of repetitive elements associated with any particular gene(s). This is the first study focusing on the gene-associated repetitive elements in the human genome. Our data showed distinct genes associated with different kinds of repetitive element and implied such combination may shape the function of these genes. Aside from the conventional view of these elements in genome evolution, results from this study offer a systemic review to facilitate exploitation of these elements in genome function.


      PubDate: 2015-07-03T15:42:05Z
       
  • In silico identification of novel IL-1β inhibitors to target
           protein–protein interfaces
    • Abstract: Publication date: Available online 30 June 2015
      Source:Computational Biology and Chemistry
      Author(s): Sobia Ahsan Halim , Muhammad Jawad , Muhammad Ilyas , Zulfiqar Mir , Atif Anwar Mirza , Tayyab Husnain
      Interleukin-1β is a drug target in rheumatoid arthritis and several auto-immune disorders. In this study, a set of 48 compounds with the determined IC50 values were used for QSAR analysis by MOE. The QSAR model was developed by using training set of 41 compounds, based on 12 unique descriptors. Model was validated by predicting the IC50 values for a test set of seven compounds. A correlation analysis was carried out comparing the statistics of the measured IC50 values with predicted ones. Subsequently, model was used for the screening of a large data set of 7,397,957 compounds obtained from “Drugs Now” category of ZINC database. The activities of those compounds were predicted by developed model. 708,960 compounds that showed best predicted activities were chosen for further studies. Additionally this set of 708,960 compounds was screened by pharmacophore modelling that led to the retrieval of 1809 molecules. Finally docking of 1809 molecules was conducted at the IL-1β receptor binding site using MOE and FRED docking program. Several new compounds were predicted as IL-1β inhibitors in silico. This study provides valuable insight for designing more potent and selective inhibitors for the treatment of inflammatory diseases.
      Graphical abstract image

      PubDate: 2015-07-03T15:42:05Z
       
  • Acknowledgment to reviewers
    • Abstract: Publication date: August 2015
      Source:Computational Biology and Chemistry, Volume 57




      PubDate: 2015-07-03T15:42:05Z
       
  • Identification of potential Tpx inhibitors against pathogen-host
           interactions
    • Abstract: Publication date: Available online 2 July 2015
      Source:Computational Biology and Chemistry
      Author(s): Utku Deniz , Kutlu O. Ulgen , Elif Ozkirimli
      Yersinia organisms cause many infectious diseases by invading human cells and delivering their virulence factors via the type three secretion system (T3SS). One alternative strategy in the fight against these pathogenic organisms is to interfere with their T3SS. Previous studies demonstrated that thiol peroxidase, Tpx is functional in the assembly of T3SS and its inhibition by salicylidene acylhydrazides prevents the secretion of pathogenic effectors. In this study, the aim was to identify potential inhibitors of Tpx using an integrated approach starting with high throughput virtual screening and ending with molecular dynamics simulations of selected ligands. Virtual screening of ZINC database of 500 000 compounds via ligand-based and structure-based pharmacophore models retrieved 10 000 hits. The structure-based pharmacophore model was validated using high-throughput virtual screening (HTVS). After multistep docking (SP and XP), common scaffolds were used to find common substructures and the ligand binding poses were optimized using induced fit docking. The stability of the protein – ligand complex was examined with molecular dynamics simulations and the binding free energy of the complex was calculated. As a final outcome eight compounds with different chemotypes were proposed as potential inhibitors for Tpx. The eight ligands identified by a detailed virtual screening protocol can serve as leads in future drug design efforts against the destructive actions of pathogenic bacteria.
      Graphical abstract image

      PubDate: 2015-07-03T15:42:05Z
       
  • Tumor stratification by a novel graph-regularized bi-clique finding
           algorithm
    • Abstract: Publication date: August 2015
      Source:Computational Biology and Chemistry, Volume 57
      Author(s): Amin Ahmadi Adl , Xiaoning Qian
      Due to involved disease mechanisms, many complex diseases such as cancer, demonstrate significant heterogeneity with varying behaviors, including different survival time, treatment responses, and recurrence rates. The aim of tumor stratification is to identify disease subtypes, which is an important first step towards precision medicine. Recent advances in profiling a large number of molecular variables such as in The Cancer Genome Atlas (TCGA), have enabled researchers to implement computational methods, including traditional clustering and bi-clustering algorithms, to systematically analyze high-throughput molecular measurements to identify tumor subtypes as well as their corresponding associated biomarkers. In this study we discuss critical issues and challenges in existing computational approaches for tumor stratification. We show that the problem can be formulated as finding densely connected sub-graphs (bi-cliques) in a bipartite graph representation of genomic data. We propose a novel algorithm that takes advantage of prior biology knowledge through a gene–gene interaction network to find such sub-graphs, which helps simultaneously identify both tumor subtypes and their corresponding genetic markers. Our experimental results show that our proposed method outperforms current state-of-the-art methods for tumor stratification.


      PubDate: 2015-07-03T15:42:05Z
       
  • BagReg: Protein inference through machine learning
    • Abstract: Publication date: August 2015
      Source:Computational Biology and Chemistry, Volume 57
      Author(s): Can Zhao , Dao Liu , Ben Teng , Zengyou He
      Protein inference from the identified peptides is of primary importance in the shotgun proteomics. The target of protein inference is to identify whether each candidate protein is truly present in the sample. To date, many computational methods have been proposed to solve this problem. However, there is still no method that can fully utilize the information hidden in the input data. In this article, we propose a learning-based method named BagReg for protein inference. The method firstly artificially extracts five features from the input data, and then chooses each feature as the class feature to separately build models to predict the presence probabilities of proteins. Finally, the weak results from five prediction models are aggregated to obtain the final result. We test our method on six public available data sets. The experimental results show that our method is superior to the state-of-the-art protein inference algorithms.
      Graphical abstract image Highlights

      PubDate: 2015-07-03T15:42:05Z
       
  • A semi-supervised learning approach for RNA secondary structure prediction
    • Abstract: Publication date: August 2015
      Source:Computational Biology and Chemistry, Volume 57
      Author(s): Haruka Yonemoto , Kiyoshi Asai , Michiaki Hamada
      RNA secondary structure prediction is a key technology in RNA bioinformatics. Most algorithms for RNA secondary structure prediction use probabilistic models, in which the model parameters are trained with reliable RNA secondary structures. Because of the difficulty of determining RNA secondary structures by experimental procedures, such as NMR or X-ray crystal structural analyses, there are still many RNA sequences that could be useful for training whose secondary structures have not been experimentally determined. In this paper, we introduce a novel semi-supervised learning approach for training parameters in a probabilistic model of RNA secondary structures in which we employ not only RNA sequences with annotated secondary structures but also ones with unknown secondary structures. Our model is based on a hybrid of generative (stochastic context-free grammars) and discriminative models (conditional random fields) that has been successfully applied to natural language processing. Computational experiments indicate that the accuracy of secondary structure prediction is improved by incorporating RNA sequences with unknown secondary structures into training. To our knowledge, this is the first study of a semi-supervised learning approach for RNA secondary structure prediction. This technique will be useful when the number of reliable structures is limited.


      PubDate: 2015-07-03T15:42:05Z
       
  • DNA entropy reveals a significant difference in complexity between
           housekeeping and tissue specific gene promoters
    • Abstract: Publication date: October 2015
      Source:Computational Biology and Chemistry, Volume 58
      Author(s): David Thomas , Chris Finan , Melanie J. Newport , Susan Jones
      Background The complexity of DNA can be quantified using estimates of entropy. Variation in DNA complexity is expected between the promoters of genes with different transcriptional mechanisms; namely housekeeping (HK) and tissue specific (TS). The former are transcribed constitutively to maintain general cellular functions, and the latter are transcribed in restricted tissue and cells types for specific molecular events. It is known that promoter features in the human genome are related to tissue specificity, but this has been difficult to quantify on a genomic scale. If entropy effectively quantifies DNA complexity, calculating the entropies of HK and TS gene promoters as profiles may reveal significant differences. Results Entropy profiles were calculated for a total dataset of 12,003 human gene promoters and for 501 housekeeping (HK) and 587 tissue specific (TS) human gene promoters. The mean profiles show the TS promoters have a significantly lower entropy (p <2.2e−16) than HK gene promoters. The entropy distributions for the 3 datasets show that promoter entropies could be used to identify novel HK genes. Conclusion Functional features comprise DNA sequence patterns that are non-random and hence they have lower entropies. The lower entropy of TS gene promoters can be explained by a higher density of positive and negative regulatory elements, required for genes with complex spatial and temporary expression.
      Graphical abstract image

      PubDate: 2015-07-03T15:42:05Z
       
  • A novel biological role for nsLTP2 from Oriza sativa: Potential
           incorporation with anticancer agents, nucleosides and their analogues
    • Abstract: Publication date: October 2015
      Source:Computational Biology and Chemistry, Volume 58
      Author(s): Mojtaba Tousheh , Fatemeh Zahra Darvishi , Mehran Miroliaei
      Development of a protein-based drug delivery system has major impact on the efficacy and bioavailability of unstable and water insoluble drugs. In the present study, the binding modes of a nonspecific lipid transfer protein (nsLTP2) from Oryza sativa with various nucleosides and analogous molecules were identified. The 3-D structure of the protein was designed and validated using modeler 9.13, Molegro virtual docker and procheck tool, respectively. The binding affinity and strength of interactions, key contributing residues and specificity toward the substrates were accomplished by computational docking and model prediction. The protein presented high affinity to acyclovir and vidarabine as purine-analogous drugs. Binding affinity is influenced by the core template and functional groups of the ligands which are structurally different cause the variation of interaction energies with nsLTP2. Nonetheless, all the evaluated analogous drugs occupy the proximity space at the nsLTP active site with high similarity in their binding modes. Our findings hold great promise for the future applications of nsLTPs in various aspects of pharmaceutical science and molecular biology.
      Graphical abstract image

      PubDate: 2015-07-03T15:42:05Z
       
  • A time and space complexity reduction for coevolutionary analysis of trees
           generated under both a Yule and Uniform model
    • Abstract: Publication date: August 2015
      Source:Computational Biology and Chemistry, Volume 57
      Author(s): Benjamin Drinkwater , Michael A. Charleston
      The topology or shape of evolutionary trees and their unbalanced nature has been a long standing area of interest in the field of phylogenetics. Coevolutionary analysis, which considers the evolutionary relationships between a pair of phylogenetic trees, has to date not considered leveraging this unbalanced nature as a means to reduce the complexity of coevolutionary analysis. In this work we apply previous analyses of tree shapes to improve the efficiency of inferring coevolutionary events. In particular, we use this prior research to derive a new data structure for inferring coevolutionary histories. Our new data structure is proven to provide a reduction in the time and space required to infer coevolutionary events. It is integrated into an existing framework for coevolutionary analysis and has been validated using both synthetic and previously published biological data sets. This proposed data structure performs twice as fast as algorithms implemented using existing data structures with no degradation in the algorithm's accuracy. As the coevolutionary data sets increase in size so too does the running time reduction provided by the newly proposed data structure. This is due to our data structure offering a logarithmic time and space complexity improvement. As a result, the proposed update to existing coevolutionary analysis algorithms outlined herein should enable the inference of larger coevolutionary systems in the future.
      Graphical abstract image Highlights

      PubDate: 2015-07-03T15:42:05Z
       
  • A new vision of evaluating gene expression signatures
    • Abstract: Publication date: August 2015
      Source:Computational Biology and Chemistry, Volume 57
      Author(s): Hung-Ming Lai , Celal Özturk , Andreas Albrecht , Kathleen Steinhöfel
      Gene expression profiles based on high-throughput technologies contribute to molecular classifications of different cell lines and consequently to clinical diagnostic tests for cancer types and other diseases. Statistical techniques and dimension reduction methods have been devised for identifying minimal gene subset with maximal discriminative power. For sets of in silico candidate genes, assuming a unique gene signature or performing a parsimonious signature evaluation seems to be too restrictive in the context of in vitro signature validation. This is mainly due to the high complexity of largely correlated expression measurements and the existence of various oncogenic pathways. Consequently, it might be more advantageous to identify and evaluate multiple gene signatures with a similar good predictive power, which are referred to as near-optimal signatures, to be made available for biological validation. For this purpose we propose the bead-chain-plot approach originating from swarm intelligence techniques, and a small scale computational experiment is conducted in order to convey our vision. We simulate the acquisition of candidate genes by using a small pool of differentially expressed genes derived from microarray-based CNS tumour data. The application of the bead-chain-plot provides experimental evidence for improved classifications by using near-optimal signatures in validation procedures.
      Graphical abstract image Highlights

      PubDate: 2015-07-03T15:42:05Z
       
  • Ancestral population genomics using coalescence hidden Markov models and
           heuristic optimisation algorithms
    • Abstract: Publication date: Available online 4 March 2015
      Source:Computational Biology and Chemistry
      Author(s): Jade Cheng , Thomas Mailund
      With full genome data from several closely related species now readily available, we have the ultimate data for demographic inference. Exploiting these full genomes, however, requires models that can explicitly model recombination along alignments of full chromosomal length. Over the last decade a class of models, based on the sequential Markov coalescence model combined with hidden Markov models, has been developed and used to make inference in simple demographic scenarios. To move forward to more complex demographic modelling we need better and more automated ways of specifying these models and efficient optimisation algorithms for inferring the parameters in complex and often high-dimensional models. In this paper we present a framework for building such coalescence hidden Markov models for pairwise alignments and present results for using heuristic optimisation algorithms for parameter estimation. We show that we can build more complex demographic models than our previous frameworks and that we obtain more accurate parameter estimates using heuristic optimisation algorithms than when using our previous gradient based approaches. Our new framework provides a flexible way of constructing coalescence hidden Markov models almost automatically. While estimating parameters in more complex models is still challenging we show that using heuristic optimisation algorithms we still get a fairly good accuracy.


      PubDate: 2015-03-08T19:03:40Z
       
 
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
Fax: +00 44 (0)131 4513327
 
About JournalTOCs
API
Help
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-2015