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Special Issue of the 1st International Applied Bioinformatics Conference (iABC'21)
Get PDFAllmer J, Elloumi M, Comin M, Hofestädt R. Special Issue of the 1st International Applied Bioinformatics Conference (iABC'21). Journal of Integrative Bioinformatics. 2021;18(4)
Theoretical and practical analyses in metagenomic sequence classification
No full textMetagenomics is the study of genomic sequences in a heterogeneous microbial sample taken, e.g. from soil, water, human microbiome and skin. One of the primary objectives of metagenomic studies is to assign a taxonomic identity to each read sequenced from a sample and then to estimate the abundance of the known clades. With ever-increasing metagenomic datasets obtained from high-throughput sequencing technologies readily available nowadays, several fast and accurate methods have been developed that can work with reasonable computing requirements. Here we provide an overview of the state-of-the-art methods for the classification of metagenomic sequences, especially highlighting theoretical factors that seem to correlate well with practical factors, and could therefore be useful in the choice or development of a new method in experimental contexts. In particular, we emphasize that the information derived from the known genomes and eventually used in the learning and classification processes may create several experimental issues—mostly based on the amount of information used in the processes and its uniqueness, significance, and redundancy,—and some of these issues are intrinsic both in current alignment-based approaches and in compositional ones. This entails the need to develop efficient alignment-free methods that overcome such problems by combining the learning and classification processes in a single framework
Analysing Author Self-citations in Computer Science Publications
Get PDFIn scientific papers, citations refer to relevant previous work in order to underline the current line of argumentation, compare to other work and/or avoid repetition in writing. Self-citations, e.g. authors citing own previous work might have the same motivation but have also gained negative attention w.r.t. unjustified improvement of scientific performance indicators. Previous studies on self-citations do not provide a detailed analysis in the domain of computer science. In this work, we analyse the prevalence of self-citations in the DBLP, a digital library for computer science. We find, that approx. 10% of all citations are self-citations, while the rates vary with year after publication and the position of the author in the list as well as with the gender of the lead author. Further, we find that C-ranked venues have the highest incoming self-citation rate, while the outgoing rate is stable across all ranks
Space-Adaptive and Workload-Aware Replication and Partitioning for Distributed RDF Triple Stores
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Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Prediction of Protein Quaternary Structures
Get PDFDetermination of the protein structure and understanding its function is essential for any relevant medical, engineering, or pharmaceutical applications. Therefore, the study of quaternary structure of proteins, despite all the obstacles in acquiring data from large macromolecular assemblies, is one of the major goals in biomolecular sciences. This chapter discusses respectively protein structure prediction, template-based predictions, critical assessment of protein structure prediction (CASP), and quaternary structure prediction. Homology modeling and threading methods are two types of template-based approaches. The homology modeling method needs to have the homologous protein structure as template and threading methods are a new approach in fold recognition, in which the tool attempts to fit the sequence in the known structures. A few sequence-based computational methods have been developed for the prediction of protein quaternary structure using statistical models or machine learning methods. © 2016 John Wiley & Sons, Inc
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