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Publisher: Oxford University Press   (Total: 392 journals)

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Showing 1 - 200 of 392 Journals sorted alphabetically
Acta Biochimica et Biophysica Sinica     Hybrid Journal   (Followers: 5, SJR: 0.881, h-index: 38)
Adaptation     Hybrid Journal   (Followers: 8, SJR: 0.111, h-index: 4)
Advances in Nutrition     Hybrid Journal   (Followers: 42, SJR: 2.075, h-index: 36)
Aesthetic Surgery J.     Hybrid Journal   (Followers: 6, SJR: 1.538, h-index: 35)
African Affairs     Hybrid Journal   (Followers: 65, SJR: 1.512, h-index: 46)
Age and Ageing     Hybrid Journal   (Followers: 86, SJR: 1.611, h-index: 107)
Alcohol and Alcoholism     Hybrid Journal   (Followers: 18, SJR: 0.935, h-index: 80)
American Entomologist     Full-text available via subscription   (Followers: 6)
American Historical Review     Hybrid Journal   (Followers: 145, SJR: 0.652, h-index: 43)
American J. of Agricultural Economics     Hybrid Journal   (Followers: 40, SJR: 1.441, h-index: 77)
American J. of Clinical Nutrition     Hybrid Journal   (Followers: 146, SJR: 3.771, h-index: 262)
American J. of Epidemiology     Hybrid Journal   (Followers: 169, SJR: 3.047, h-index: 201)
American J. of Hypertension     Hybrid Journal   (Followers: 25, SJR: 1.397, h-index: 111)
American J. of Jurisprudence     Hybrid Journal   (Followers: 18)
American J. of Legal History     Full-text available via subscription   (Followers: 8, SJR: 0.151, h-index: 7)
American Law and Economics Review     Hybrid Journal   (Followers: 27, SJR: 0.824, h-index: 23)
American Literary History     Hybrid Journal   (Followers: 15, SJR: 0.185, h-index: 22)
Analysis     Hybrid Journal   (Followers: 21)
Animal Frontiers     Hybrid Journal  
Annals of Behavioral Medicine     Hybrid Journal   (Followers: 14, SJR: 2.112, h-index: 98)
Annals of Botany     Hybrid Journal   (Followers: 35, SJR: 1.912, h-index: 124)
Annals of Occupational Hygiene     Hybrid Journal   (Followers: 29, SJR: 0.837, h-index: 57)
Annals of Oncology     Hybrid Journal   (Followers: 48, SJR: 4.362, h-index: 173)
Annals of the Entomological Society of America     Full-text available via subscription   (Followers: 8, SJR: 0.642, h-index: 53)
Annals of Work Exposures and Health     Hybrid Journal  
AoB Plants     Open Access   (Followers: 4, SJR: 0.78, h-index: 10)
Applied Economic Perspectives and Policy     Hybrid Journal   (Followers: 17, SJR: 0.884, h-index: 31)
Applied Linguistics     Hybrid Journal   (Followers: 55, SJR: 1.749, h-index: 63)
Applied Mathematics Research eXpress     Hybrid Journal   (Followers: 1, SJR: 0.779, h-index: 11)
Arbitration Intl.     Full-text available via subscription   (Followers: 19)
Arbitration Law Reports and Review     Hybrid Journal   (Followers: 13)
Archives of Clinical Neuropsychology     Hybrid Journal   (Followers: 30, SJR: 0.96, h-index: 71)
Aristotelian Society Supplementary Volume     Hybrid Journal   (Followers: 3, SJR: 0.102, h-index: 20)
Arthropod Management Tests     Hybrid Journal   (Followers: 2)
Astronomy & Geophysics     Hybrid Journal   (Followers: 42, SJR: 0.144, h-index: 15)
Behavioral Ecology     Hybrid Journal   (Followers: 51, SJR: 1.698, h-index: 92)
Bioinformatics     Hybrid Journal   (Followers: 285, SJR: 4.643, h-index: 271)
Biology Methods and Protocols     Hybrid Journal  
Biology of Reproduction     Full-text available via subscription   (Followers: 9, SJR: 1.646, h-index: 149)
Biometrika     Hybrid Journal   (Followers: 20, SJR: 2.801, h-index: 90)
BioScience     Hybrid Journal   (Followers: 30, SJR: 2.374, h-index: 154)
Bioscience Horizons : The National Undergraduate Research J.     Open Access   (Followers: 1, SJR: 0.213, h-index: 9)
Biostatistics     Hybrid Journal   (Followers: 17, SJR: 1.955, h-index: 55)
BJA : British J. of Anaesthesia     Hybrid Journal   (Followers: 162, SJR: 2.314, h-index: 133)
BJA Education     Hybrid Journal   (Followers: 64, SJR: 0.272, h-index: 20)
Brain     Hybrid Journal   (Followers: 68, SJR: 6.097, h-index: 264)
Briefings in Bioinformatics     Hybrid Journal   (Followers: 44, SJR: 4.086, h-index: 73)
Briefings in Functional Genomics     Hybrid Journal   (Followers: 3, SJR: 1.771, h-index: 50)
British J. for the Philosophy of Science     Hybrid Journal   (Followers: 33, SJR: 1.267, h-index: 38)
British J. of Aesthetics     Hybrid Journal   (Followers: 26, SJR: 0.217, h-index: 18)
British J. of Criminology     Hybrid Journal   (Followers: 584, SJR: 1.373, h-index: 62)
British J. of Social Work     Hybrid Journal   (Followers: 86, SJR: 0.771, h-index: 53)
British Medical Bulletin     Hybrid Journal   (Followers: 7, SJR: 1.391, h-index: 84)
British Yearbook of Intl. Law     Hybrid Journal   (Followers: 30)
Bulletin of the London Mathematical Society     Hybrid Journal   (Followers: 4, SJR: 1.474, h-index: 31)
Cambridge J. of Economics     Hybrid Journal   (Followers: 61, SJR: 0.957, h-index: 59)
Cambridge J. of Regions, Economy and Society     Hybrid Journal   (Followers: 9, SJR: 1.067, h-index: 22)
Cambridge Quarterly     Hybrid Journal   (Followers: 9, SJR: 0.1, h-index: 7)
Capital Markets Law J.     Hybrid Journal   (Followers: 2)
Carcinogenesis     Hybrid Journal   (Followers: 2, SJR: 2.439, h-index: 167)
Cardiovascular Research     Hybrid Journal   (Followers: 12, SJR: 2.897, h-index: 175)
Cerebral Cortex     Hybrid Journal   (Followers: 44, SJR: 4.827, h-index: 192)
CESifo Economic Studies     Hybrid Journal   (Followers: 17, SJR: 0.501, h-index: 19)
Chemical Senses     Hybrid Journal   (Followers: 1, SJR: 1.436, h-index: 76)
Children and Schools     Hybrid Journal   (Followers: 5, SJR: 0.211, h-index: 18)
Chinese J. of Comparative Law     Hybrid Journal   (Followers: 4)
Chinese J. of Intl. Law     Hybrid Journal   (Followers: 22, SJR: 0.737, h-index: 11)
Chinese J. of Intl. Politics     Hybrid Journal   (Followers: 8, SJR: 1.238, h-index: 15)
Christian Bioethics: Non-Ecumenical Studies in Medical Morality     Hybrid Journal   (Followers: 10, SJR: 0.191, h-index: 8)
Classical Receptions J.     Hybrid Journal   (Followers: 25, SJR: 0.1, h-index: 3)
Clean Energy     Open Access  
Clinical Infectious Diseases     Hybrid Journal   (Followers: 62, SJR: 4.742, h-index: 261)
Clinical Kidney J.     Open Access   (Followers: 3, SJR: 0.338, h-index: 19)
Communication Theory     Hybrid Journal   (Followers: 20, SJR: 2.62, h-index: 53)
Communication, Culture & Critique     Hybrid Journal   (Followers: 25)
Community Development J.     Hybrid Journal   (Followers: 27, SJR: 0.47, h-index: 28)
Computer J.     Hybrid Journal   (Followers: 9, SJR: 0.371, h-index: 47)
Conservation Physiology     Open Access   (Followers: 2)
Contemporary Women's Writing     Hybrid Journal   (Followers: 9, SJR: 0.111, h-index: 3)
Contributions to Political Economy     Hybrid Journal   (Followers: 5, SJR: 0.313, h-index: 10)
Critical Values     Full-text available via subscription  
Current Developments in Nutrition     Open Access  
Current Legal Problems     Hybrid Journal   (Followers: 27)
Current Zoology     Full-text available via subscription   (Followers: 1, SJR: 0.999, h-index: 20)
Database : The J. of Biological Databases and Curation     Open Access   (Followers: 8, SJR: 1.068, h-index: 24)
Digital Scholarship in the Humanities     Hybrid Journal   (Followers: 13)
Diplomatic History     Hybrid Journal   (Followers: 19, SJR: 0.296, h-index: 22)
DNA Research     Open Access   (Followers: 5, SJR: 2.42, h-index: 77)
Dynamics and Statistics of the Climate System     Open Access   (Followers: 3)
Early Music     Hybrid Journal   (Followers: 15, SJR: 0.124, h-index: 11)
Economic Policy     Hybrid Journal   (Followers: 39, SJR: 2.052, h-index: 52)
ELT J.     Hybrid Journal   (Followers: 25, SJR: 1.26, h-index: 23)
English Historical Review     Hybrid Journal   (Followers: 51, SJR: 0.311, h-index: 10)
English: J. of the English Association     Hybrid Journal   (Followers: 14, SJR: 0.144, h-index: 3)
Environmental Entomology     Full-text available via subscription   (Followers: 11, SJR: 0.791, h-index: 66)
Environmental Epigenetics     Open Access   (Followers: 3)
Environmental History     Hybrid Journal   (Followers: 26, SJR: 0.197, h-index: 25)
EP-Europace     Hybrid Journal   (Followers: 2, SJR: 2.201, h-index: 71)
Epidemiologic Reviews     Hybrid Journal   (Followers: 9, SJR: 3.917, h-index: 81)
ESHRE Monographs     Hybrid Journal  
Essays in Criticism     Hybrid Journal   (Followers: 16, SJR: 0.1, h-index: 6)
European Heart J.     Hybrid Journal   (Followers: 54, SJR: 6.997, h-index: 227)
European Heart J. - Cardiovascular Imaging     Hybrid Journal   (Followers: 8, SJR: 2.044, h-index: 58)
European Heart J. - Cardiovascular Pharmacotherapy     Full-text available via subscription   (Followers: 1)
European Heart J. - Quality of Care and Clinical Outcomes     Hybrid Journal  
European Heart J. Supplements     Hybrid Journal   (Followers: 7, SJR: 0.152, h-index: 31)
European J. of Cardio-Thoracic Surgery     Hybrid Journal   (Followers: 9, SJR: 1.568, h-index: 104)
European J. of Intl. Law     Hybrid Journal   (Followers: 174, SJR: 0.722, h-index: 38)
European J. of Orthodontics     Hybrid Journal   (Followers: 4, SJR: 1.09, h-index: 60)
European J. of Public Health     Hybrid Journal   (Followers: 20, SJR: 1.284, h-index: 64)
European Review of Agricultural Economics     Hybrid Journal   (Followers: 9, SJR: 1.549, h-index: 42)
European Review of Economic History     Hybrid Journal   (Followers: 28, SJR: 0.628, h-index: 24)
European Sociological Review     Hybrid Journal   (Followers: 40, SJR: 2.061, h-index: 53)
Evolution, Medicine, and Public Health     Open Access   (Followers: 10)
Family Practice     Hybrid Journal   (Followers: 14, SJR: 1.048, h-index: 77)
Fems Microbiology Ecology     Hybrid Journal   (Followers: 10, SJR: 1.687, h-index: 115)
Fems Microbiology Letters     Hybrid Journal   (Followers: 22, SJR: 1.126, h-index: 118)
Fems Microbiology Reviews     Hybrid Journal   (Followers: 27, SJR: 7.587, h-index: 150)
Fems Yeast Research     Hybrid Journal   (Followers: 14, SJR: 1.213, h-index: 66)
Foreign Policy Analysis     Hybrid Journal   (Followers: 23, SJR: 0.859, h-index: 10)
Forest Science     Hybrid Journal   (Followers: 4, SJR: 0.872, h-index: 59)
Forestry: An Intl. J. of Forest Research     Hybrid Journal   (Followers: 14, SJR: 0.903, h-index: 44)
Forum for Modern Language Studies     Hybrid Journal   (Followers: 6, SJR: 0.108, h-index: 6)
French History     Hybrid Journal   (Followers: 32, SJR: 0.123, h-index: 10)
French Studies     Hybrid Journal   (Followers: 20, SJR: 0.119, h-index: 7)
French Studies Bulletin     Hybrid Journal   (Followers: 10, SJR: 0.102, h-index: 3)
Gastroenterology Report     Open Access   (Followers: 2)
Genome Biology and Evolution     Open Access   (Followers: 11, SJR: 3.22, h-index: 39)
Geophysical J. Intl.     Hybrid Journal   (Followers: 33, SJR: 1.839, h-index: 119)
German History     Hybrid Journal   (Followers: 22, SJR: 0.437, h-index: 13)
GigaScience     Open Access   (Followers: 3)
Global Summitry     Hybrid Journal   (Followers: 1)
Glycobiology     Hybrid Journal   (Followers: 14, SJR: 1.692, h-index: 101)
Health and Social Work     Hybrid Journal   (Followers: 54, SJR: 0.505, h-index: 40)
Health Education Research     Hybrid Journal   (Followers: 13, SJR: 0.814, h-index: 80)
Health Policy and Planning     Hybrid Journal   (Followers: 24, SJR: 1.628, h-index: 66)
Health Promotion Intl.     Hybrid Journal   (Followers: 21, SJR: 0.664, h-index: 60)
History Workshop J.     Hybrid Journal   (Followers: 28, SJR: 0.313, h-index: 20)
Holocaust and Genocide Studies     Hybrid Journal   (Followers: 26, SJR: 0.115, h-index: 13)
Human Communication Research     Hybrid Journal   (Followers: 13, SJR: 2.199, h-index: 61)
Human Molecular Genetics     Hybrid Journal   (Followers: 8, SJR: 4.288, h-index: 233)
Human Reproduction     Hybrid Journal   (Followers: 72, SJR: 2.271, h-index: 179)
Human Reproduction Update     Hybrid Journal   (Followers: 16, SJR: 4.678, h-index: 128)
Human Rights Law Review     Hybrid Journal   (Followers: 60, SJR: 0.7, h-index: 21)
ICES J. of Marine Science: J. du Conseil     Hybrid Journal   (Followers: 51, SJR: 1.233, h-index: 88)
ICSID Review     Hybrid Journal   (Followers: 12)
ILAR J.     Hybrid Journal   (Followers: 2, SJR: 1.099, h-index: 51)
IMA J. of Applied Mathematics     Hybrid Journal   (SJR: 0.329, h-index: 26)
IMA J. of Management Mathematics     Hybrid Journal   (SJR: 0.351, h-index: 20)
IMA J. of Mathematical Control and Information     Hybrid Journal   (Followers: 2, SJR: 0.661, h-index: 28)
IMA J. of Numerical Analysis - advance access     Hybrid Journal   (SJR: 2.032, h-index: 44)
Industrial and Corporate Change     Hybrid Journal   (Followers: 10, SJR: 1.37, h-index: 81)
Industrial Law J.     Hybrid Journal   (Followers: 34, SJR: 0.184, h-index: 15)
Inflammatory Bowel Diseases     Hybrid Journal   (Followers: 42, SJR: 1.994, h-index: 107)
Information and Inference     Free  
Integrative and Comparative Biology     Hybrid Journal   (Followers: 7, SJR: 1.911, h-index: 90)
Interacting with Computers     Hybrid Journal   (Followers: 10, SJR: 0.529, h-index: 59)
Interactive CardioVascular and Thoracic Surgery     Hybrid Journal   (Followers: 6, SJR: 0.743, h-index: 35)
Intl. Affairs     Hybrid Journal   (Followers: 56, SJR: 1.264, h-index: 53)
Intl. Data Privacy Law     Hybrid Journal   (Followers: 30)
Intl. Health     Hybrid Journal   (Followers: 5, SJR: 0.835, h-index: 15)
Intl. Immunology     Hybrid Journal   (Followers: 3, SJR: 1.613, h-index: 111)
Intl. J. for Quality in Health Care     Hybrid Journal   (Followers: 34, SJR: 1.593, h-index: 69)
Intl. J. of Constitutional Law     Hybrid Journal   (Followers: 63, SJR: 0.613, h-index: 19)
Intl. J. of Epidemiology     Hybrid Journal   (Followers: 186, SJR: 4.381, h-index: 145)
Intl. J. of Law and Information Technology     Hybrid Journal   (Followers: 5, SJR: 0.247, h-index: 8)
Intl. J. of Law, Policy and the Family     Hybrid Journal   (Followers: 30, SJR: 0.307, h-index: 15)
Intl. J. of Lexicography     Hybrid Journal   (Followers: 10, SJR: 0.404, h-index: 18)
Intl. J. of Low-Carbon Technologies     Open Access   (Followers: 1, SJR: 0.457, h-index: 12)
Intl. J. of Neuropsychopharmacology     Open Access   (Followers: 3, SJR: 1.69, h-index: 79)
Intl. J. of Public Opinion Research     Hybrid Journal   (Followers: 9, SJR: 0.906, h-index: 33)
Intl. J. of Refugee Law     Hybrid Journal   (Followers: 35, SJR: 0.231, h-index: 21)
Intl. J. of Transitional Justice     Hybrid Journal   (Followers: 12, SJR: 0.833, h-index: 12)
Intl. Mathematics Research Notices     Hybrid Journal   (Followers: 1, SJR: 2.052, h-index: 42)
Intl. Political Sociology     Hybrid Journal   (Followers: 35, SJR: 1.339, h-index: 19)
Intl. Relations of the Asia-Pacific     Hybrid Journal   (Followers: 22, SJR: 0.539, h-index: 17)
Intl. Studies Perspectives     Hybrid Journal   (Followers: 9, SJR: 0.998, h-index: 28)
Intl. Studies Quarterly     Hybrid Journal   (Followers: 42, SJR: 2.184, h-index: 68)
Intl. Studies Review     Hybrid Journal   (Followers: 20, SJR: 0.783, h-index: 38)
ISLE: Interdisciplinary Studies in Literature and Environment     Hybrid Journal   (Followers: 1, SJR: 0.155, h-index: 4)
ITNOW     Hybrid Journal   (Followers: 1, SJR: 0.102, h-index: 4)
J. of African Economies     Hybrid Journal   (Followers: 15, SJR: 0.647, h-index: 30)
J. of American History     Hybrid Journal   (Followers: 45, SJR: 0.286, h-index: 34)
J. of Analytical Toxicology     Hybrid Journal   (Followers: 13, SJR: 1.038, h-index: 60)
J. of Antimicrobial Chemotherapy     Hybrid Journal   (Followers: 14, SJR: 2.157, h-index: 149)
J. of Antitrust Enforcement     Hybrid Journal   (Followers: 1)
J. of Applied Poultry Research     Hybrid Journal   (Followers: 4, SJR: 0.563, h-index: 43)
J. of Biochemistry     Hybrid Journal   (Followers: 41, SJR: 1.341, h-index: 96)
J. of Burn Care & Research     Hybrid Journal   (Followers: 9, SJR: 0.713, h-index: 57)
J. of Chromatographic Science     Hybrid Journal   (Followers: 18, SJR: 0.448, h-index: 42)
J. of Church and State     Hybrid Journal   (Followers: 11, SJR: 0.167, h-index: 11)
J. of Communication     Hybrid Journal   (Followers: 50, SJR: 3.327, h-index: 82)
J. of Competition Law and Economics     Hybrid Journal   (Followers: 35, SJR: 0.442, h-index: 16)
J. of Complex Networks     Hybrid Journal   (Followers: 1, SJR: 1.165, h-index: 5)
J. of Computer-Mediated Communication     Open Access   (Followers: 26, SJR: 2.878, h-index: 80)
J. of Conflict and Security Law     Hybrid Journal   (Followers: 13, SJR: 0.196, h-index: 15)
J. of Consumer Research     Full-text available via subscription   (Followers: 41, SJR: 4.896, h-index: 121)
J. of Crohn's and Colitis     Hybrid Journal   (Followers: 9, SJR: 1.543, h-index: 37)
J. of Cybersecurity     Hybrid Journal   (Followers: 3)
J. of Deaf Studies and Deaf Education     Hybrid Journal   (Followers: 8, SJR: 0.69, h-index: 36)

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Journal Cover Bioinformatics
  [SJR: 4.643]   [H-I: 271]   [285 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1367-4803 - ISSN (Online) 1460-2059
   Published by Oxford University Press Homepage  [392 journals]
  • SimulaTE: simulating complex landscapes of transposable elements of
           populations
    • Authors: Kofler R.
      Pages: 1439 - 1439
      Abstract: Bioinformatics (2017)
      DOI : https://doi.org/10.1093/bioinformatics/btx772
      PubDate: Tue, 09 Jan 2018 00:00:00 GMT
      Issue No: Vol. 34, No. 8 (2018)
       
  • Modeling positional effects of regulatory sequences with spline
           transformations increases prediction accuracy of deep neural networks
    • Authors: Avsec Ž; Barekatain M, Cheng J, et al.
      Pages: 1261 - 1269
      Abstract: MotivationRegulatory sequences are not solely defined by their nucleic acid sequence but also by their relative distances to genomic landmarks such as transcription start site, exon boundaries or polyadenylation site. Deep learning has become the approach of choice for modeling regulatory sequences because of its strength to learn complex sequence features. However, modeling relative distances to genomic landmarks in deep neural networks has not been addressed.ResultsHere we developed spline transformation, a neural network module based on splines to flexibly and robustly model distances. Modeling distances to various genomic landmarks with spline transformations significantly increased state-of-the-art prediction accuracy of in vivo RNA-binding protein binding sites for 120 out of 123 proteins. We also developed a deep neural network for human splice branchpoint based on spline transformations that outperformed the current best, already distance-based, machine learning model. Compared to piecewise linear transformation, as obtained by composition of rectified linear units, spline transformation yields higher prediction accuracy as well as faster and more robust training. As spline transformation can be applied to further quantities beyond distances, such as methylation or conservation, we foresee it as a versatile component in the genomics deep learning toolbox.Availability and implementationSpline transformation is implemented as a Keras layer in the CONCISE python package: https://github.com/gagneurlab/concise. Analysis code is available at https://github.com/gagneurlab/Manuscript_Avsec_Bioinformatics_2017.Contactavsec@in.tum.de or gagneur@in.tum.deSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: Thu, 16 Nov 2017 00:00:00 GMT
      DOI: 10.1093/bioinformatics/btx727
      Issue No: Vol. 34, No. 8 (2017)
       
  • MOCASSIN-prot: a multi-objective clustering approach for protein
           similarity networks
    • Authors: Keel B; Deng B, Moriyama E.
      Pages: 1270 - 1277
      Abstract: MotivationProteins often include multiple conserved domains. Various evolutionary events including duplication and loss of domains, domain shuffling, as well as sequence divergence contribute to generating complexities in protein structures, and consequently, in their functions. The evolutionary history of proteins is hence best modeled through networks that incorporate information both from the sequence divergence and the domain content. Here, a game-theoretic approach proposed for protein network construction is adapted into the framework of multi-objective optimization, and extended to incorporate clustering refinement procedure.ResultsThe new method, MOCASSIN-prot, was applied to cluster multi-domain proteins from ten genomes. The performance of MOCASSIN-prot was compared against two protein clustering methods, Markov clustering (TRIBE-MCL) and spectral clustering (SCPS). We showed that compared to these two methods, MOCASSIN-prot, which uses both domain composition and quantitative sequence similarity information, generates fewer false positives. It achieves more functionally coherent protein clusters and better differentiates protein families.Availability and implementationMOCASSIN-prot, implemented in Perl and Matlab, is freely available at http://bioinfolab.unl.edu/emlab/MOCASSINprot.Contactemoriyama2@unl.eduSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: Fri, 24 Nov 2017 00:00:00 GMT
      DOI: 10.1093/bioinformatics/btx755
      Issue No: Vol. 34, No. 8 (2017)
       
  • Identifying functionally informative evolutionary sequence profiles
    • Authors: Gil N; Fiser A.
      Pages: 1278 - 1286
      Abstract: MotivationMultiple sequence alignments (MSAs) can provide essential input to many bioinformatics applications, including protein structure prediction and functional annotation. However, the optimal selection of sequences to obtain biologically informative MSAs for such purposes is poorly explored, and has traditionally been performed manually.ResultsWe present Selection of Alignment by Maximal Mutual Information (SAMMI), an automated, sequence-based approach to objectively select an optimal MSA from a large set of alternatives sampled from a general sequence database search. The hypothesis of this approach is that the mutual information among MSA columns will be maximal for those MSAs that contain the most diverse set possible of the most structurally and functionally homogeneous protein sequences. SAMMI was tested to select MSAs for functional site residue prediction by analysis of conservation patterns on a set of 435 proteins obtained from protein–ligand (peptides, nucleic acids and small substrates) and protein–protein interaction databases.Availability and implementation: A freely accessible program, including source code, implementing SAMMI is available at https://github.com/nelsongil92/SAMMI.git.Contactandras.fiser@einstein.yu.eduSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: Fri, 01 Dec 2017 00:00:00 GMT
      DOI: 10.1093/bioinformatics/btx779
      Issue No: Vol. 34, No. 8 (2017)
       
  • FROGS: Find, Rapidly, OTUs with Galaxy Solution
    • Authors: Escudié F; Auer L, Bernard M, et al.
      Pages: 1287 - 1294
      Abstract: MotivationMetagenomics leads to major advances in microbial ecology and biologists need user friendly tools to analyze their data on their own.ResultsThis Galaxy-supported pipeline, called FROGS, is designed to analyze large sets of amplicon sequences and produce abundance tables of Operational Taxonomic Units (OTUs) and their taxonomic affiliation. The clustering uses Swarm. The chimera removal uses VSEARCH, combined with original cross-sample validation. The taxonomic affiliation returns an innovative multi-affiliation output to highlight databases conflicts and uncertainties. Statistical results and numerous graphical illustrations are produced along the way to monitor the pipeline. FROGS was tested for the detection and quantification of OTUs on real and in silico datasets and proved to be rapid, robust and highly sensitive. It compares favorably with the widespread mothur, UPARSE and QIIME.Availability and implementationSource code and instructions for installation: https://github.com/geraldinepascal/FROGS.git. A companion website: http://frogs.toulouse.inra.fr.Contactgeraldine.pascal@inra.frSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: Thu, 07 Dec 2017 00:00:00 GMT
      DOI: 10.1093/bioinformatics/btx791
      Issue No: Vol. 34, No. 8 (2017)
       
  • DeepSF: deep convolutional neural network for mapping protein sequences to
           folds
    • Authors: Hou J; Adhikari B, Cheng J.
      Pages: 1295 - 1303
      Abstract: MotivationProtein fold recognition is an important problem in structural bioinformatics. Almost all traditional fold recognition methods use sequence (homology) comparison to indirectly predict the fold of a target protein based on the fold of a template protein with known structure, which cannot explain the relationship between sequence and fold. Only a few methods had been developed to classify protein sequences into a small number of folds due to methodological limitations, which are not generally useful in practice.ResultsWe develop a deep 1D-convolution neural network (DeepSF) to directly classify any protein sequence into one of 1195 known folds, which is useful for both fold recognition and the study of sequence–structure relationship. Different from traditional sequence alignment (comparison) based methods, our method automatically extracts fold-related features from a protein sequence of any length and maps it to the fold space. We train and test our method on the datasets curated from SCOP1.75, yielding an average classification accuracy of 75.3%. On the independent testing dataset curated from SCOP2.06, the classification accuracy is 73.0%. We compare our method with a top profile–profile alignment method—HHSearch on hard template-based and template-free modeling targets of CASP9-12 in terms of fold recognition accuracy. The accuracy of our method is 12.63–26.32% higher than HHSearch on template-free modeling targets and 3.39–17.09% higher on hard template-based modeling targets for top 1, 5 and 10 predicted folds. The hidden features extracted from sequence by our method is robust against sequence mutation, insertion, deletion and truncation, and can be used for other protein pattern recognition problems such as protein clustering, comparison and ranking.Availability and implementationThe DeepSF server is publicly available at: http://iris.rnet.missouri.edu/DeepSF/.Contactchengji@missouri.eduSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: Fri, 08 Dec 2017 00:00:00 GMT
      DOI: 10.1093/bioinformatics/btx780
      Issue No: Vol. 34, No. 8 (2017)
       
  • New algorithms to represent complex pseudoknotted RNA structures in
           dot-bracket notation
    • Authors: Antczak M; Popenda M, Zok T, et al.
      Pages: 1304 - 1312
      Abstract: MotivationUnderstanding the formation, architecture and roles of pseudoknots in RNA structures are one of the most difficult challenges in RNA computational biology and structural bioinformatics. Methods predicting pseudoknots typically perform this with poor accuracy, often despite experimental data incorporation. Existing bioinformatic approaches differ in terms of pseudoknots’ recognition and revealing their nature. A few ways of pseudoknot classification exist, most common ones refer to a genus or order. Following the latter one, we propose new algorithms that identify pseudoknots in RNA structure provided in BPSEQ format, determine their order and encode in dot-bracket-letter notation. The proposed encoding aims to illustrate the hierarchy of RNA folding.ResultsNew algorithms are based on dynamic programming and hybrid (combining exhaustive search and random walk) approaches. They evolved from elementary algorithm implemented within the workflow of RNA FRABASE 1.0, our database of RNA structure fragments. They use different scoring functions to rank dissimilar dot-bracket representations of RNA structure. Computational experiments show an advantage of new methods over the others, especially for large RNA structures.Availability and implementationPresented algorithms have been implemented as new functionality of RNApdbee webserver and are ready to use at http://rnapdbee.cs.put.poznan.pl.Contactmszachniuk@cs.put.poznan.plSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: Mon, 11 Dec 2017 00:00:00 GMT
      DOI: 10.1093/bioinformatics/btx783
      Issue No: Vol. 34, No. 8 (2017)
       
  • ASElux: an ultra-fast and accurate allelic reads counter
    • Authors: Miao Z; Alvarez M, Pajukanta P, et al.
      Pages: 1313 - 1320
      Abstract: MotivationMapping bias causes preferential alignment to the reference allele, forming a major obstacle in allele-specific expression (ASE) analysis. The existing methods, such as simulation and SNP-aware alignment, are either inaccurate or relatively slow. To fast and accurately count allelic reads for ASE analysis, we developed a novel approach, ASElux, which utilizes the personal SNP information and counts allelic reads directly from unmapped RNA-sequence (RNA-seq) data. ASElux significantly reduces runtime by disregarding reads outside single nucleotide polymorphisms (SNPs) during the alignment.ResultsWhen compared to other tools on simulated and experimental data, ASElux achieves a higher accuracy on ASE estimation than non-SNP-aware aligners and requires a much shorter time than the benchmark SNP-aware aligner, GSNAP with just a slight loss in performance. ASElux can process 40 million read-pairs from an RNA-sequence (RNA-seq) sample and count allelic reads within 10 min, which is comparable to directly counting the allelic reads from alignments based on other tools. Furthermore, processing an RNA-seq sample using ASElux in conjunction with a general aligner, such as STAR, is more accurate and still ∼4× faster than STAR + WASP, and ∼33× faster than the lead SNP-aware aligner, GSNAP, making ASElux ideal for ASE analysis of large-scale transcriptomic studies. We applied ASElux to 273 lung RNA-seq samples from GTEx and identified a splice-QTL rs11078928 in lung which explains the mechanism underlying an asthma GWAS SNP rs11078927. Thus, our analysis demonstrated ASE as a highly powerful complementary tool to cis-expression quantitative trait locus (eQTL) analysis.Availability and implementationThe software can be downloaded from https://github.com/abl0719/ASElux.Contactzmiao@ucla.edu or a5ko@ucla.eduSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: Thu, 23 Nov 2017 00:00:00 GMT
      DOI: 10.1093/bioinformatics/btx762
      Issue No: Vol. 34, No. 8 (2017)
       
  • Meta-analytic principal component analysis in integrative omics
           application
    • Authors: Kim S; Kang D, Huo Z, et al.
      Pages: 1321 - 1328
      Abstract: MotivationWith the prevalent usage of microarray and massively parallel sequencing, numerous high-throughput omics datasets have become available in the public domain. Integrating abundant information among omics datasets is critical to elucidate biological mechanisms. Due to the high-dimensional nature of the data, methods such as principal component analysis (PCA) have been widely applied, aiming at effective dimension reduction and exploratory visualization.ResultsIn this article, we combine multiple omics datasets of identical or similar biological hypothesis and introduce two variations of meta-analytic framework of PCA, namely MetaPCA. Regularization is further incorporated to facilitate sparse feature selection in MetaPCA. We apply MetaPCA and sparse MetaPCA to simulations, three transcriptomic meta-analysis studies in yeast cell cycle, prostate cancer, mouse metabolism and a TCGA pan-cancer methylation study. The result shows improved accuracy, robustness and exploratory visualization of the proposed framework.Availability and implementationAn R package MetaPCA is available online. (http://tsenglab.biostat.pitt.edu/software.htm).Contactctseng@pitt.eduSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: Thu, 23 Nov 2017 00:00:00 GMT
      DOI: 10.1093/bioinformatics/btx765
      Issue No: Vol. 34, No. 8 (2017)
       
  • Classifying next-generation sequencing data using a zero-inflated Poisson
           model
    • Authors: Zhou Y; Wan X, Zhang B, et al.
      Pages: 1329 - 1335
      Abstract: MotivationWith the development of high-throughput techniques, RNA-sequencing (RNA-seq) is becoming increasingly popular as an alternative for gene expression analysis, such as RNAs profiling and classification. Identifying which type of diseases a new patient belongs to with RNA-seq data has been recognized as a vital problem in medical research. As RNA-seq data are discrete, statistical methods developed for classifying microarray data cannot be readily applied for RNA-seq data classification. Witten proposed a Poisson linear discriminant analysis (PLDA) to classify the RNA-seq data in 2011. Note, however, that the count datasets are frequently characterized by excess zeros in real RNA-seq or microRNA sequence data (i.e. when the sequence depth is not enough or small RNAs with the length of 18–30 nucleotides). Therefore, it is desired to develop a new model to analyze RNA-seq data with an excess of zeros.ResultsIn this paper, we propose a Zero-Inflated Poisson Logistic Discriminant Analysis (ZIPLDA) for RNA-seq data with an excess of zeros. The new method assumes that the data are from a mixture of two distributions: one is a point mass at zero, and the other follows a Poisson distribution. We then consider a logistic relation between the probability of observing zeros and the mean of the genes and the sequencing depth in the model. Simulation studies show that the proposed method performs better than, or at least as well as, the existing methods in a wide range of settings. Two real datasets including a breast cancer RNA-seq dataset and a microRNA-seq dataset are also analyzed, and they coincide with the simulation results that our proposed method outperforms the existing competitors.Availability and implementationThe software is available at http://www.math.hkbu.edu.hk/∼tongt.Contactxwan@comp.hkbu.edu.hk or tongt@hkbu.edu.hkSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: Mon, 27 Nov 2017 00:00:00 GMT
      DOI: 10.1093/bioinformatics/btx768
      Issue No: Vol. 34, No. 8 (2017)
       
  • Sequential feature selection and inference using multi-variate random
           forests
    • Authors: Mayer J; Rahman R, Ghosh S, et al.
      Pages: 1336 - 1344
      Abstract: MotivationRandom forest (RF) has become a widely popular prediction generating mechanism. Its strength lies in its flexibility, interpretability and ability to handle large number of features, typically larger than the sample size. However, this methodology is of limited use if one wishes to identify statistically significant features. Several ranking schemes are available that provide information on the relative importance of the features, but there is a paucity of general inferential mechanism, particularly in a multi-variate set up. We use the conditional inference tree framework to generate a RF where features are deleted sequentially based on explicit hypothesis testing. The resulting sequential algorithm offers an inferentially justifiable, but model-free, variable selection procedure. Significant features are then used to generate predictive RF. An added advantage of our methodology is that both variable selection and prediction are based on conditional inference framework and hence are coherent.ResultsWe illustrate the performance of our Sequential Multi-Response Feature Selection approach through simulation studies and finally apply this methodology on Genomics of Drug Sensitivity for Cancer dataset to identify genetic characteristics that significantly impact drug sensitivities. Significant set of predictors obtained from our method are further validated from biological perspective.Availability and implementationhttps://github.com/jomayer/SMuRFContactsouparno.ghosh@ttu.eduSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: Mon, 18 Dec 2017 00:00:00 GMT
      DOI: 10.1093/bioinformatics/btx784
      Issue No: Vol. 34, No. 8 (2017)
       
  • SANA NetGO: a combinatorial approach to using Gene Ontology (GO) terms to
           score network alignments
    • Authors: Hayes W; Mamano N.
      Pages: 1345 - 1352
      Abstract: MotivationGene Ontology (GO) terms are frequently used to score alignments between protein–protein interaction (PPI) networks. Methods exist to measure GO similarity between proteins in isolation, but proteins in a network alignment are not isolated: each pairing is dependent on every other via the alignment itself. Existing measures fail to take into account the frequency of GO terms across networks, instead imposing arbitrary rules on when to allow GO terms.ResultsHere we develop NetGO, a new measure that naturally weighs infrequent, informative GO terms more heavily than frequent, less informative GO terms, without arbitrary cutoffs, instead downweighting GO terms according to their frequency in the networks being aligned. This is a global measure applicable only to alignments, independent of pairwise GO measures, in the same sense that the edge-based EC or S3 scores are global measures of topological similarity independent of pairwise topological similarities. We demonstrate the superiority of NetGO in alignments of predetermined quality and show that NetGO correlates with alignment quality better than any existing GO-based alignment measures. We also demonstrate that NetGO provides a measure of taxonomic similarity between species, consistent with existing taxonomic measuresa feature not shared with existing GObased network alignment measures. Finally, we re-score alignments produced by almost a dozen aligners from a previous study and show that NetGO does a better job at separating good alignments from bad ones.Availability and implementationAvailable as part of SANA.Contactwhayes@uci.eduSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: Thu, 07 Dec 2017 00:00:00 GMT
      DOI: 10.1093/bioinformatics/btx716
      Issue No: Vol. 34, No. 8 (2017)
       
  • Global proteomics profiling improves drug sensitivity prediction: results
           from a multi-omics, pan-cancer modeling approach
    • Authors: Ali M; Khan S, Wennerberg K, et al.
      Pages: 1353 - 1362
      Abstract: MotivationProteomics profiling is increasingly being used for molecular stratification of cancer patients and cell-line panels. However, systematic assessment of the predictive power of large-scale proteomic technologies across various drug classes and cancer types is currently lacking. To that end, we carried out the first pan-cancer, multi-omics comparative analysis of the relative performance of two proteomic technologies, targeted reverse phase protein array (RPPA) and global mass spectrometry (MS), in terms of their accuracy for predicting the sensitivity of cancer cells to both cytotoxic chemotherapeutics and molecularly targeted anticancer compounds.ResultsOur results in two cell-line panels demonstrate how MS profiling improves drug response predictions beyond that of the RPPA or the other omics profiles when used alone. However, frequent missing MS data values complicate its use in predictive modeling and required additional filtering, such as focusing on completely measured or known oncoproteins, to obtain maximal predictive performance. Rather strikingly, the two proteomics profiles provided complementary predictive signal both for the cytotoxic and targeted compounds. Further, information about the cellular-abundance of primary target proteins was found critical for predicting the response of targeted compounds, although the non-target features also contributed significantly to the predictive power. The clinical relevance of the selected protein markers was confirmed in cancer patient data. These results provide novel insights into the relative performance and optimal use of the widely applied proteomic technologies, MS and RPPA, which should prove useful in translational applications, such as defining the best combination of omics technologies and marker panels for understanding and predicting drug sensitivities in cancer patients.Availability and implementationProcessed datasets, R as well as Matlab implementations of the methods are available at https://github.com/mehr-een/bemkl-rbps.Contactmehreen.ali@helsinki.fi or tero.aittokallio@fimm.fiSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: Mon, 27 Nov 2017 00:00:00 GMT
      DOI: 10.1093/bioinformatics/btx766
      Issue No: Vol. 34, No. 8 (2017)
       
  • Model-based design of bistable cell factories for metabolic engineering
    • Authors: Srinivasan S; Cluett W, Mahadevan R.
      Pages: 1363 - 1371
      Abstract: MotivationMetabolism can exhibit dynamic phenomena like bistability due to the presence of regulatory motifs like the positive feedback loop. As cell factories, microorganisms with bistable metabolism can have a high and a low product flux at the two stable steady states, respectively. The exclusion of metabolic regulation and network dynamics limits the ability of pseudo-steady state stoichiometric models to detect the presence of bistability, and reliably assess the outcomes of design perturbations to metabolic networks.ResultsUsing kinetic models of metabolism, we assess the change in the bistable characteristics of the network, and suggest designs based on perturbations to the positive feedback loop to enable the network to produce at its theoretical maximum rate. We show that the most optimal production design in parameter space, for a small bistable metabolic network, may exist at the boundary of the bistable region separating it from the monostable region of low product fluxes. The results of our analysis can be broadly applied to other bistable metabolic networks with similar positive feedback network topologies. This can complement existing model-based design strategies by providing a smaller number of feasible designs that need to be tested in vivo.Availability and implementationhttp://lmse.biozone.utoronto.ca/downloads/Contactkrishna.mahadevan@utoronto.caSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: Wed, 06 Dec 2017 00:00:00 GMT
      DOI: 10.1093/bioinformatics/btx769
      Issue No: Vol. 34, No. 8 (2017)
       
  • Efficiently counting all orbits of graphlets of any order in a graph using
           autogenerated equations
    • Authors: Melckenbeeck I; Audenaert P, Colle D, et al.
      Pages: 1372 - 1380
      Abstract: MotivationGraphlets are a useful tool to determine a graph’s small-scale structure. Finding them is exponentially hard with respect to the number of nodes in each graphlet. Therefore, equations can be used to reduce the size of graphlets that need to be enumerated to calculate the number of each graphlet touching each node. Hočevar and Demšar first introduced such equations, which were derived manually, and an algorithm that uses them, but only graphlets with four or five nodes can be counted this way.ResultsWe present a new algorithm for orbit counting, which is applicable to graphlets of any order. This algorithm uses a tree structure to simplify finding orbits, and stabilizers and symmetry-breaking constraints to ensure correctness. This method gives a significant speedup compared to a brute force counting method and can count orbits beyond the capacity of other available tools.Availability and implementationAn implementation of the algorithm can be found at https://github.com/biointec/jesse.Contactpieter.audenaert@ugent.be
      PubDate: Fri, 24 Nov 2017 00:00:00 GMT
      DOI: 10.1093/bioinformatics/btx758
      Issue No: Vol. 34, No. 8 (2017)
       
  • An attention-based BiLSTM-CRF approach to document-level chemical named
           entity recognition
    • Authors: Luo L; Yang Z, Yang P, et al.
      Pages: 1381 - 1388
      Abstract: MotivationIn biomedical research, chemical is an important class of entities, and chemical named entity recognition (NER) is an important task in the field of biomedical information extraction. However, most popular chemical NER methods are based on traditional machine learning and their performances are heavily dependent on the feature engineering. Moreover, these methods are sentence-level ones which have the tagging inconsistency problem.ResultsIn this paper, we propose a neural network approach, i.e. attention-based bidirectional Long Short-Term Memory with a conditional random field layer (Att-BiLSTM-CRF), to document-level chemical NER. The approach leverages document-level global information obtained by attention mechanism to enforce tagging consistency across multiple instances of the same token in a document. It achieves better performances with little feature engineering than other state-of-the-art methods on the BioCreative IV chemical compound and drug name recognition (CHEMDNER) corpus and the BioCreative V chemical-disease relation (CDR) task corpus (the F-scores of 91.14 and 92.57%, respectively).Availability and implementationData and code are available at https://github.com/lingluodlut/Att-ChemdNER.Contactyangzh@dlut.edu.cn or wangleibihami@gmail.comSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: Fri, 24 Nov 2017 00:00:00 GMT
      DOI: 10.1093/bioinformatics/btx761
      Issue No: Vol. 34, No. 8 (2017)
       
  • LitPathExplorer: a confidence-based visual text analytics tool for
           exploring literature-enriched pathway models
    • Authors: Soto A; Zerva C, Batista-Navarro R, et al.
      Pages: 1389 - 1397
      Abstract: MotivationPathway models are valuable resources that help us understand the various mechanisms underpinning complex biological processes. Their curation is typically carried out through manual inspection of published scientific literature to find information relevant to a model, which is a laborious and knowledge-intensive task. Furthermore, models curated manually cannot be easily updated and maintained with new evidence extracted from the literature without automated support.ResultsWe have developed LitPathExplorer, a visual text analytics tool that integrates advanced text mining, semi-supervised learning and interactive visualization, to facilitate the exploration and analysis of pathway models using statements (i.e. events) extracted automatically from the literature and organized according to levels of confidence. LitPathExplorer supports pathway modellers and curators alike by: (i) extracting events from the literature that corroborate existing models with evidence; (ii) discovering new events which can update models; and (iii) providing a confidence value for each event that is automatically computed based on linguistic features and article metadata. Our evaluation of event extraction showed a precision of 89% and a recall of 71%. Evaluation of our confidence measure, when used for ranking sampled events, showed an average precision ranging between 61 and 73%, which can be improved to 95% when the user is involved in the semi-supervised learning process. Qualitative evaluation using pair analytics based on the feedback of three domain experts confirmed the utility of our tool within the context of pathway model exploration.Availability and implementationLitPathExplorer is available at http://nactem.ac.uk/LitPathExplorer_BI/.Contactsophia.ananiadou@manchester.ac.ukSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: Fri, 08 Dec 2017 00:00:00 GMT
      DOI: 10.1093/bioinformatics/btx774
      Issue No: Vol. 34, No. 8 (2017)
       
  • glactools: a command-line toolset for the management of genotype
           likelihoods and allele counts
    • Authors: Renaud G.
      Pages: 1398 - 1400
      Abstract: MotivationResearch projects involving population genomics routinely need to store genotyping information, population allele counts, combine files from different samples, query the data and export it to various formats. This is often done using bespoke in-house scripts, which cannot be easily adapted to new projects and seldom constitute reproducible workflows.ResultsWe introduce glactools, a set of command-line utilities that can import data from genotypes or population-wide allele counts into an intermediate representation, compute various operations on it and export the data to several file formats used by population genetics software. This intermediate format can take two forms, one to store per-individual genotype likelihoods and a second for allele counts from one or more individuals. glactools allows users to perform operations such as intersecting datasets, merging individuals into populations, creating subsets, perform queries (e.g. return sites where a given population does not share an allele with a second one) and compute summary statistics to answer biologically relevant questions.Availability and implementationglactools is freely available for use under the GPL. It requires a C ++ compiler and the htslib library. The source code and the instructions about how to download test data are available on the website (https://grenaud.github.io/glactools/).Contactgabriel.reno@gmail.comSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: Mon, 27 Nov 2017 00:00:00 GMT
      DOI: 10.1093/bioinformatics/btx749
      Issue No: Vol. 34, No. 8 (2017)
       
  • SAPP: functional genome annotation and analysis through a semantic
           framework using FAIR principles
    • Authors: Koehorst J; van Dam J, Saccenti E, et al.
      Pages: 1401 - 1403
      Abstract: SummaryTo unlock the full potential of genome data and to enhance data interoperability and reusability of genome annotations we have developed SAPP, a Semantic Annotation Platform with Provenance. SAPP is designed as an infrastructure supporting FAIR de novo computational genomics but can also be used to process and analyze existing genome annotations. SAPP automatically predicts, tracks and stores structural and functional annotations and associated dataset- and element-wise provenance in a Linked Data format, thereby enabling information mining and retrieval with Semantic Web technologies. This greatly reduces the administrative burden of handling multiple analysis tools and versions thereof and facilitates multi-level large scale comparative analysis.Availability and implementationSAPP is written in JAVA and freely available at https://gitlab.com/sapp and runs on Unix-like operating systems. The documentation, examples and a tutorial are available at https://sapp.gitlab.io.Contactjasperkoehorst@gmail.com or peter.schaap@wur.nl
      PubDate: Thu, 23 Nov 2017 00:00:00 GMT
      DOI: 10.1093/bioinformatics/btx767
      Issue No: Vol. 34, No. 8 (2017)
       
  • ASAR: visual analysis of metagenomes in R
    • Authors: Orakov A; Sakenova N, Sorokin A, et al.
      Pages: 1404 - 1405
      Abstract: MotivationFunctional and taxonomic analyses are critical steps in understanding interspecific interactions within microbial communities. Currently, such analyses are run separately, which complicates interpretation of results. Here we present the ASAR interactive tool for simultaneous analysis of metagenomic data in three dimensions: taxonomy, function, metagenome.ResultsAn interactive data analysis tool for selection, aggregation and visualization of metagenomic data is presented. Functional analysis with a SEED hierarchy and pathway diagram based on KEGG orthology based upon MG-RAST annotation results is available.Availability and implementationSource code of the ASAR is accessible at GitHub (https://github.com/Askarbek-orakov/ASAR).Contactaskarbek.orakov@nu.edu.kz or goryanin@gmail.com
      PubDate: Fri, 01 Dec 2017 00:00:00 GMT
      DOI: 10.1093/bioinformatics/btx775
      Issue No: Vol. 34, No. 8 (2017)
       
  • Gene Graphics: a genomic neighborhood data visualization web application
    • Authors: Harrison K; Crécy-Lagard V, Zallot R.
      Pages: 1406 - 1408
      Abstract: SummaryThe examination of gene neighborhood is an integral part of comparative genomics but no tools to produce publication quality graphics of gene clusters are available. Gene Graphics is a straightforward web application for creating such visuals. Supported inputs include National Center for Biotechnology Information gene and protein identifiers with automatic fetching of neighboring information, GenBank files and data extracted from the SEED database. Gene representations can be customized for many parameters including gene and genome names, colors and sizes. Gene attributes can be copied and pasted for rapid and user-friendly customization of homologous genes between species. In addition to Portable Network Graphics and Scalable Vector Graphics, produced representations can be exported as Tagged Image File Format or Encapsulated PostScript, formats that are standard for publication. Hands-on tutorials with real life examples inspired from publications are available for training.Availability and implementationGene Graphics is freely available at https://katlabs.cc/genegraphics/ and source code is hosted at https://github.com/katlabs/genegraphics.Contactkatherinejh@ufl.edu or remizallot@ufl.eduSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: Thu, 07 Dec 2017 00:00:00 GMT
      DOI: 10.1093/bioinformatics/btx793
      Issue No: Vol. 34, No. 8 (2017)
       
  • Delta: a new web-based 3D genome visualization and analysis platform
    • Authors: Tang B; Li F, Li J, et al.
      Pages: 1409 - 1410
      Abstract: SummaryDelta is an integrative visualization and analysis platform to facilitate visually annotating and exploring the 3D physical architecture of genomes. Delta takes Hi-C or ChIA-PET contact matrix as input and predicts the topologically associating domains and chromatin loops in the genome. It then generates a physical 3D model which represents the plausible consensus 3D structure of the genome. Delta features a highly interactive visualization tool which enhances the integration of genome topology/physical structure with extensive genome annotation by juxtaposing the 3D model with diverse genomic assay outputs. Finally, by visually comparing the 3D model of the β-globin gene locus and its annotation, we speculated a plausible transitory interaction pattern in the locus. Experimental evidence was found to support this speculation by literature survey. This served as an example of intuitive hypothesis testing with the help of Delta.Availability and implementationDelta is freely accessible from http://delta.big.ac.cn, and the source code is available at https://github.com/zhangzhwlab/delta.Contactzhangzhihua@big.ac.cnSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: Fri, 15 Dec 2017 00:00:00 GMT
      DOI: 10.1093/bioinformatics/btx805
      Issue No: Vol. 34, No. 8 (2017)
       
  • Nephele: a cloud platform for simplified, standardized and reproducible
           microbiome data analysis
    • Authors: Weber N; Liou D, Dommer J, et al.
      Pages: 1411 - 1413
      Abstract: MotivationWidespread interest in the study of the microbiome has resulted in data proliferation and the development of powerful computational tools. However, many scientific researchers lack the time, training, or infrastructure to work with large datasets or to install and use command line tools.ResultsThe National Institute of Allergy and Infectious Diseases (NIAID) has created Nephele, a cloud-based microbiome data analysis platform with standardized pipelines and a simple web interface for transforming raw data into biological insights. Nephele integrates common microbiome analysis tools as well as valuable reference datasets like the healthy human subjects cohort of the Human Microbiome Project (HMP). Nephele is built on the Amazon Web Services cloud, which provides centralized and automated storage and compute capacity, thereby reducing the burden on researchers and their institutions.Availability and implementationhttps://nephele.niaid.nih.gov and https://github.com/niaid/NepheleContactdarrell.hurt@nih.gov
      PubDate: Tue, 03 Oct 2017 00:00:00 GMT
      DOI: 10.1093/bioinformatics/btx617
      Issue No: Vol. 34, No. 8 (2017)
       
  • Bicycle: a bioinformatics pipeline to analyze bisulfite sequencing data
    • Authors: Graña O; López-Fernández H, Fdez-Riverola F, et al.
      Pages: 1414 - 1415
      Abstract: SummaryHigh-throughput sequencing of bisulfite-converted DNA is a technique used to measure DNA methylation levels. Although a considerable number of computational pipelines have been developed to analyze such data, none of them tackles all the peculiarities of the analysis together, revealing limitations that can force the user to manually perform additional steps needed for a complete processing of the data. This article presents bicycle, an integrated, flexible analysis pipeline for bisulfite sequencing data. Bicycle analyzes whole genome bisulfite sequencing data, targeted bisulfite sequencing data and hydroxymethylation data. To show how bicycle overtakes other available pipelines, we compared them on a defined number of features that are summarized in a table. We also tested bicycle with both simulated and real datasets, to show its level of performance, and compared it to different state-of-the-art methylation analysis pipelines.Availability and implementationBicycle is publicly available under GNU LGPL v3.0 license at http://www.sing-group.org/bicycle. Users can also download a customized Ubuntu LiveCD including bicycle and other bisulfite sequencing data pipelines compared here. In addition, a docker image with bicycle and its dependencies, which allows a straightforward use of bicycle in any platform (e.g. Linux, OS X or Windows), is also available.Contactograna@cnio.es or dgpena@uvigo.esSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: Fri, 01 Dec 2017 00:00:00 GMT
      DOI: 10.1093/bioinformatics/btx778
      Issue No: Vol. 34, No. 8 (2017)
       
  • DaMiRseq—an R/Bioconductor package for data mining of RNA-Seq data:
           normalization, feature selection and classification
    • Authors: Chiesa M; Colombo G, Piacentini L.
      Pages: 1416 - 1418
      Abstract: SummaryRNA-Seq is becoming the technique of choice for high-throughput transcriptome profiling, which, besides class comparison for differential expression, promises to be an effective and powerful tool for biomarker discovery. However, a systematic analysis of high-dimensional genomic data is a demanding task for such a purpose. DaMiRseq offers an organized, flexible and convenient framework to remove noise and bias, select the most informative features and perform accurate classification.Availability and implementationDaMiRseq is developed for the R environment (R ≥ 3.4) and is released under GPL (≥2) License. The package runs on Windows, Linux and Macintosh operating systems and is freely available to non-commercial users at the Bioconductor open-source, open-development software project repository (https://bioconductor.org/packages/DaMiRseq/). In compliance with Bioconductor standards, the authors ensure stable package maintenance through software and documentation updates.Contactluca.piacentini@ccfm.itSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: Mon, 11 Dec 2017 00:00:00 GMT
      DOI: 10.1093/bioinformatics/btx795
      Issue No: Vol. 34, No. 8 (2017)
       
  • SimulaTE: simulating complex landscapes of transposable elements of
           populations
    • Authors: Kofler R.
      Pages: 1419 - 1420
      Abstract: MotivationEstimating the abundance of transposable elements (TEs) in populations (or tissues) promises to answer many open research questions. However, progress is hampered by the lack of concordance between different approaches for TE identification and thus potentially unreliable results.ResultsTo address this problem, we developed SimulaTE a tool that generates TE landscapes for populations using a newly developed domain specific language (DSL). The simple syntax of our DSL allows for easily building even complex TE landscapes that have, for example, nested, truncated and highly diverged TE insertions. Reads may be simulated for the populations using different sequencing technologies (PacBio, Illumina paired-ends) and strategies (sequencing individuals and pooled populations). The comparison between the expected (i.e. simulated) and the observed results will guide researchers in finding the most suitable approach for a particular research question.Availability and implementationSimulaTE is implemented in Python and available at https://sourceforge.net/projects/simulates/. Manual https://sourceforge.net/p/simulates/wiki/Home/#manual; Test data and tutorials https://sourceforge.net/p/simulates/wiki/Home/#walkthrough; Validation https://sourceforge.net/p/simulates/wiki/Home/#validation.Contactrobert.kofler@vetmeduni.ac.at
      PubDate: Mon, 27 Nov 2017 00:00:00 GMT
      DOI: 10.1093/bioinformatics/btx772
      Issue No: Vol. 34, No. 8 (2017)
       
  • GenSSI 2.0: multi-experiment structural identifiability analysis of SBML
           models
    • Authors: Ligon T; Fröhlich F, Chiş O, et al.
      Pages: 1421 - 1423
      Abstract: MotivationMathematical modeling using ordinary differential equations is used in systems biology to improve the understanding of dynamic biological processes. The parameters of ordinary differential equation models are usually estimated from experimental data. To analyze a priori the uniqueness of the solution of the estimation problem, structural identifiability analysis methods have been developed.ResultsWe introduce GenSSI 2.0, an advancement of the software toolbox GenSSI (Generating Series for testing Structural Identifiability). GenSSI 2.0 is the first toolbox for structural identifiability analysis to implement Systems Biology Markup Language import, state/parameter transformations and multi-experiment structural identifiability analysis. In addition, GenSSI 2.0 supports a range of MATLAB versions and is computationally more efficient than its previous version, enabling the analysis of more complex models.Availability and implementationGenSSI 2.0 is an open-source MATLAB toolbox and available at https://github.com/genssi-developer/GenSSI.Contactthomas.ligon@physik.uni-muenchen.de or jan.hasenauer@helmholtz-muenchen.deSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: Thu, 30 Nov 2017 00:00:00 GMT
      DOI: 10.1093/bioinformatics/btx735
      Issue No: Vol. 34, No. 8 (2017)
       
  • Reproducible and flexible simulation experiments with ML-Rules and SESSL
    • Authors: Warnke T; Helms T, Uhrmacher A.
      Pages: 1424 - 1427
      Abstract: SummaryThe modeling language ML-Rules allows specifying and simulating complex systems biology models at multiple levels of organization. The development of such simulation models involves a wide variety of simulation experiments and the replicability of generated simulation results requires suitable means for documenting simulation experiments. Embedded domain-specific languages, such as SESSL, cater to both requirements. With SESSL, the user can integrate diverse simulation experimentation methods and third-party software components into an executable, readable simulation experiment specification. A newly developed SESSL binding for ML-Rules exploits these features of SESSL, opening up new possibilities for executing and documenting simulation experiments with ML-Rules models.Availability and implementationML-Rules is implemented in Java, SESSL and its bindings are implemented in Scala. The source code is available under open-source licenses:ML-Rules               git.informatik.uni-rostock.de/mosi/mlrules2ML-Rules Quickstart (Graphical Editor) git.informatik.uni-rostock.de/mosi/mlrules2-quickstartSESSL               git.informatik.uni-rostock.de/mosi/sessl and sessl.orgSESSL Quickstart (Experiment Template) git.informatik.uni-rostock.de/mosi/sessl-quickstartFurthermore, Maven-compatible compiled packages of ML-Rules, SESSL, and the SESSL bindings are available from the Maven Central Repository at maven.org (org.sessl:* and org.jamesii: mlrules).Contacttom.warnke@uni-rostock.deSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: Thu, 23 Nov 2017 00:00:00 GMT
      DOI: 10.1093/bioinformatics/btx741
      Issue No: Vol. 34, No. 8 (2017)
       
  • CytoCtrlAnalyser: a Cytoscape app for biomolecular network controllability
           analysis
    • Authors: Wu L; Li M, Wang J, et al.
      Pages: 1428 - 1430
      Abstract: SummaryStudying the controllability of biomolecular networks can result in profound knowledge about molecular biological systems. However, there is no comprehensive and easy-to-use platform for analyzing controllability of biomolecular networks although various algorithms for analyzing complex network controllability have been proposed recently. In this application note, we develop the CytoCtrlAnalyser which is a Cytoscape app to provide a comprehensive platform for analyzing controllability of biomolecular networks. Nine algorithms have been integrated in CytoCtrlAnalyser. With network topologies and customized control settings imported into CytoCtrlAnalyser, users can identify the steering nodes which should be actuated by input control signals for achieving different control objectives as well as investigate the importance of nodes from different perspectives in the controllability of networks. CytoCtrlAnalyser offers a tool for many promising applications, such as identification of potential drug targets or biologically important nodes in biomolecular networks.Availability and implementationFreely available for downloading at http://apps.cytoscape.org/apps/cytoctrlanalyser.Contactfaw341@mail.usask.caSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: Thu, 23 Nov 2017 00:00:00 GMT
      DOI: 10.1093/bioinformatics/btx764
      Issue No: Vol. 34, No. 8 (2017)
       
  • Rcupcake: an R package for querying and analyzing biomedical data through
           the BD2K PIC-SURE RESTful API
    • Authors: Gutiérrez-Sacristán A; Guedj R, Korodi G, et al.
      Pages: 1431 - 1432
      Abstract: MotivationIn the era of big data and precision medicine, the number of databases containing clinical, environmental, self-reported and biochemical variables is increasing exponentially. Enabling the experts to focus on their research questions rather than on computational data management, access and analysis is one of the most significant challenges nowadays.ResultsWe present Rcupcake, an R package that contains a variety of functions for leveraging different databases through the BD2K PIC-SURE RESTful API and facilitating its query, analysis and interpretation. The package offers a variety of analysis and visualization tools, including the study of the phenotype co-occurrence and prevalence, according to multiple layers of data, such as phenome, exposome or genome.Availability and implementationThe package is implemented in R and is available under Mozilla v2 license from GitHub (https://github.com/hms-dbmi/Rcupcake). Two reproducible case studies are also available (https://github.com/hms-dbmi/Rcupcake-case-studies/blob/master/SSCcaseStudy_v01.ipynb, https://github.com/hms-dbmi/Rcupcake-case-studies/blob/master/NHANEScaseStudy_v01.ipynb).Contactpaul_avillach@hms.harvard.eduSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: Mon, 18 Dec 2017 00:00:00 GMT
      DOI: 10.1093/bioinformatics/btx788
      Issue No: Vol. 34, No. 8 (2017)
       
  • FUn: a framework for interactive visualizations of large, high-dimensional
           datasets on the web
    • Authors: Probst D; Reymond J.
      Pages: 1433 - 1435
      Abstract: MotivationDuring the past decade, big data have become a major tool in scientific endeavors. Although statistical methods and algorithms are well-suited for analyzing and summarizing enormous amounts of data, the results do not allow for a visual inspection of the entire data. Current scientific software, including R packages and Python libraries such as ggplot2, matplotlib and plot.ly, do not support interactive visualizations of datasets exceeding 100 000 data points on the web. Other solutions enable the web-based visualization of big data only through data reduction or statistical representations. However, recent hardware developments, especially advancements in graphical processing units, allow for the rendering of millions of data points on a wide range of consumer hardware such as laptops, tablets and mobile phones. Similar to the challenges and opportunities brought to virtually every scientific field by big data, both the visualization of and interaction with copious amounts of data are both demanding and hold great promise.ResultsHere we present FUn, a framework consisting of a client (Faerun) and server (Underdark) module, facilitating the creation of web-based, interactive 3D visualizations of large datasets, enabling record level visual inspection. We also introduce a reference implementation providing access to SureChEMBL, a database containing patent information on more than 17 million chemical compounds.Availability and implementationThe source code and the most recent builds of Faerun and Underdark, Lore.js and the data preprocessing toolchain used in the reference implementation, are available on the project website (http://doc.gdb.tools/fun/).Contactdaniel.probst@dcb.unibe.ch or jean-louis.reymond@dcb.unibe.ch
      PubDate: Fri, 24 Nov 2017 00:00:00 GMT
      DOI: 10.1093/bioinformatics/btx760
      Issue No: Vol. 34, No. 8 (2017)
       
  • Curatr: a web application for creating, curating and sharing a mass
           spectral library
    • Authors: Palmer A; Phapale P, Fay D, et al.
      Pages: 1436 - 1438
      Abstract: SummaryWe have developed a web application curatr for the rapid generation of high quality mass spectral fragmentation libraries from liquid-chromatography mass spectrometry datasets. Curatr handles datasets from single or multiplexed standards and extracts chromatographic profiles and potential fragmentation spectra for multiple adducts. An intuitive interface helps users to select high quality spectra that are stored along with searchable molecular information, the providence of each standard and experimental metadata. Curatr supports exports to several standard formats for use with third party software or submission to repositories. We demonstrate the use of curatr to generate the EMBL Metabolomics Core Facility spectral library http://curatr.mcf.embl.de.Availability and implementationSource code and example data are at http://github.com/alexandrovteam/curatr/.Contactpalmer@embl.deSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: Thu, 14 Dec 2017 00:00:00 GMT
      DOI: 10.1093/bioinformatics/btx786
      Issue No: Vol. 34, No. 8 (2017)
       
  • A novel approach based on KATZ measure to predict associations of human
           microbiota with non-infectious diseases
    • Authors: Chen X; Huang Y, You Z, et al.
      Pages: 1440 - 1440
      Abstract: Bioinformatics (2017) 33 (5): 733–739.
      PubDate: Thu, 14 Dec 2017 00:00:00 GMT
      DOI: 10.1093/bioinformatics/btx773
      Issue No: Vol. 34, No. 8 (2017)
       
 
 
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