Exploring heterogeneous biological databases: Tools and applications

1998 ◽  
pp. 499-513 ◽  
Author(s):  
Anthony S. Kosky ◽  
I -Min A. Chen ◽  
Victor M. Markowitz ◽  
Ernest Szeto
Keyword(s):  
Biological Databases

Deviations from Ultrametricity in Phage Protein Distances

2009 ◽  
Vol 16 (01) ◽  
pp. 75-84
Author(s):  
Chad Wagner ◽  
Anna Salamon ◽  
Robert A. Edwards ◽  
Forest Rohwer ◽  
Peter Salamon
Keyword(s):  
Biological Databases ◽  
Threshold Values ◽  
Phage Protein ◽  
Phage Proteins ◽  
Qualitative Changes

Distances in biological databases are known not to be ultrametric. Deviations from ultrametricity can however reveal useful features of biodata. In the present study we examine deviations from ultrametricity of the distances between known phage proteins quantified in two senses: (1) the failure of triangles to be isosceles and (2) failure of every point to be the center of any sphere in which it resides. The deviations from these two ultrametric properties undergo qualitative changes as a function of the distance. Below we describe these changes and how they can be observed. We further argue that the distances at which the qualitative changes take place reveal intrinsic scales in the dataset. Such scales are important for choosing threshold values of the distance in various algorithms and reveal natural chunking of the data that can be used to decide clade levels in phage phylogeny.

scholarly journals Improved ontology for eukaryotic single-exon coding sequences in biological databases

Database ◽  
2018 ◽  
Vol 2018 ◽  
Author(s):  
Roddy Jorquera ◽  
Carolina González ◽  
Philip Clausen ◽  
Bent Petersen ◽  
David S Holmes
Keyword(s):  
Biological Databases ◽  
Coding Sequences

scholarly journals Tripal and Galaxy: supporting reproducible scientific workflows for community biological databases

Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Shawna Spoor ◽  
Connor Wytko ◽  
Brian Soto ◽  
Ming Chen ◽  
Abdullah Almsaeed ◽  
...  
Keyword(s):  
Workflow Management ◽  
Application Programming Interface ◽  
Scientific Workflow ◽  
Biological Databases ◽  
Web Based ◽  
Job Management ◽  
The Galaxy ◽  
Application Programming ◽  
Computational Resources ◽  
Programming Interface

Abstract Online biological databases housing genomics, genetic and breeding data can be constructed using the Tripal toolkit. Tripal is an open-source, internationally developed framework that implements FAIR data principles and is meant to ease the burden of constructing such websites for research communities. Use of a common, open framework improves the sustainability and manageability of such as site. Site developers can create extensions for their site and in turn share those extensions with others. One challenge that community databases often face is the need to provide tools for their users that analyze increasingly larger datasets using multiple software tools strung together in a scientific workflow on complicated computational resources. The Tripal Galaxy module, a ‘plug-in’ for Tripal, meets this need through integration of Tripal with the Galaxy Project workflow management system. Site developers can create workflows appropriate to the needs of their community using Galaxy and then share those for execution on their Tripal sites via automatically constructed, but configurable, web forms or using an application programming interface to power web-based analytical applications. The Tripal Galaxy module helps reduce duplication of effort by allowing site developers to spend time constructing workflows and building their applications rather than rebuilding infrastructure for job management of multi-step applications.

Diversity and distribution of Phanaeini (Coleoptera: Scarabaeidae: Scarabaeinae) in Mexico

Zootaxa ◽  
2017 ◽  
Vol 4358 (2) ◽  
pp. 271 ◽  
Author(s):  
VIRIDIANA LIZARDO ◽  
FEDERICO ESCOBAR ◽  
OCTAVIO ROJAS-SOTO
Keyword(s):  
Relevant Literature ◽  
Volcanic Belt ◽  
Field Studies ◽  
Distribution Data ◽  
Biological Databases ◽  
Distribution Models ◽  
Distribution Maps ◽  
Distribution Ranges ◽  
Starting Point ◽  
Sierra Madre

In this study, we systematized available distribution data, obtained from biological databases and relevant literature, for Mexican species belonging to the tribe Phanaeini. The main objectives were to provide an overall description of the distribution records in biological collections, to detect potential sampling biases, to describe the seasonality of collections and to obtain species distribution models using the Desktop GARP algorithm. A total of 5,562 records, corresponding to 32 species in Mexico, were compiled, including the recently described Phanaeus zoque Moctezuma & Halffter, 2017. This compilation includes 784 unique collection records at 325 localities. These records were mainly distributed along the Trans-Mexican Volcanic Belt, the Sierra Madre Oriental and Sierra Madre Occidental mountain ranges and throughout the states of Chiapas and Veracruz. The Mexican High Plateau, the state of Tlaxcala and the Yucatan Peninsula are lacking in records. Distribution maps were created for species of three genera (Phanaeus MacLeay, 1819, Coprophanaeus Olsoufieff, 1924, and Sulcophanaeus Olsoufieff, 1924) and for 29 species present in Mexico. These species distributions are largely delimited by geomorphological features and vegetation types and coincide with expert descriptions of this tribe; some species show expanded distribution ranges. These maps provide a starting point for further analyses, the planning of future field studies, and the verification of possible new species in the Mexican territory. 

scholarly journals Machine learning can differentiate venom toxins from other proteins having non-toxic physiological functions

2016 ◽  
Vol 2 ◽  
pp. e90 ◽  
Author(s):  
Ranko Gacesa ◽  
David J. Barlow ◽  
Paul F. Long
Keyword(s):  
Machine Learning ◽  
Sequence Data ◽  
Biological Data ◽  
Biological Databases ◽  
Web Based ◽  
Physiological Functions ◽  
Link Type ◽  
Venom Toxins ◽  
Venomous Animals ◽  
Toxin Protein

Ascribing function to sequence in the absence of biological data is an ongoing challenge in bioinformatics. Differentiating the toxins of venomous animals from homologues having other physiological functions is particularly problematic as there are no universally accepted methods by which to attribute toxin function using sequence data alone. Bioinformatics tools that do exist are difficult to implement for researchers with little bioinformatics training. Here we announce a machine learning tool called ‘ToxClassifier’ that enables simple and consistent discrimination of toxins from non-toxin sequences with >99% accuracy and compare it to commonly used toxin annotation methods. ‘ToxClassifer’ also reports the best-hit annotation allowing placement of a toxin into the most appropriate toxin protein family, or relates it to a non-toxic protein having the closest homology, giving enhanced curation of existing biological databases and new venomics projects. ‘ToxClassifier’ is available for free, either to download (https://github.com/rgacesa/ToxClassifier) or to use on a web-based server (http://bioserv7.bioinfo.pbf.hr/ToxClassifier/).

scholarly journals Object-oriented parsing of biological databases with Python

2000 ◽  
Vol 16 (7) ◽  
pp. 628-638 ◽  
Author(s):  
C. Ramu ◽  
C. Gemund ◽  
T. J. Gibson
Keyword(s):  
Object Oriented ◽  
Biological Databases
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