Ali Z. Ijaz

Understanding how the environment selects a given taxon and the diversity patterns that emerge as a result of environmental filtering can dramatically improve our ability to analyse any environment in depth as well as advancing our knowledge on how the response of different taxa can impact each other and ecosystem functions. Most of the work investigating microbial biogeography has been site-specific, and logical environmental factors, rather than geographical location, may be more influential on microbial diversity. SEQenv, a novel pipeline aiming to provide environmental annotations of sequences emerged to provide a consistent description of the environmental niches using the ENVO ontology. While the pipeline provides a list of environmental terms on the basis of sample datasets and, therefore, the annotations obtained are at the dataset level, it lacks a taxa centric approach to environmental annotation. The work here describes an extension developed to enhance the SEQenv pipeline, which provided the means to directly generate environmental annotations for taxa under different contexts. 16S rDNA amplicon datasets belonging to distinct biomes were selected to illustrate the applicability of the extended SEQenv pipeline. A literature survey of the results demonstrates the immense importance of sequence level environmental annotations by illustrating the distribution of both taxa across environments as well as the various environmental sources of a specific taxon. Significantly enhancing the SEQenv pipeline in the process, this information would be valuable to any biologist seeking to understand the various taxa present in the habitat and the environment they originated from, enabling a more thorough analysis of which lineages are abundant in certain habitats and the recovery of patterns in taxon distribution across different habitats and environmental gradients.

Amplicon based taxonomic analysis, which determines the presence of microbial taxa in different environments on the basis of marker gene annotations, often uses percentage identity as the main metric to determine sequence similarity against databases. These data are then used to study the distribution of biodiversity as well as response of microbial communities to environmental conditions. However the 16S rRNA gene displays varying degrees of sequence conservation along its length and percentage identity does not fully utilize this information. Additionally, the prevalent usage of Operational Taxonomic Unit, or OTUs is not without its own issues and may lead to a reduction in annotation capability of the system. Hence a novel approach to taxonomic annotation is needed. Here we introduce a new taxonomic annotation pipeline, TaxaSE, which utilizes Shannon entropy to quantify evolutionary conservation within 16S rDNA sequences for enhanced taxonomic annotations. Furthermore, the system is capable of annotation of individual sequences in order to improve fine grain taxonomic annotations. We present both in-silico comparison of the new similarity metric with percentage identity, as well as comparison with the popular QIIME pipeline. The results demonstrate the new similarity metric achieves better performance especially at lower taxa levels. Furthermore, the pipeline is able to extract more fine grain taxonomic annotations compared to QIIME. These exhibit not only the effectiveness of the new pipeline but also highlight the need to shift away from both percentage identity and OTU based approaches for ecological projects.

Amplicon based taxonomic analysis, which determines the presence of microbial taxa in different environments on the basis of marker gene annotations, often uses percentage identity as the main metric to determine sequence similarity against databases. These data are then used to study the distribution of biodiversity as well as response of microbial communities to environmental conditions. However the 16S rRNA gene displays varying degrees of sequence conservation along its length and percentage identity does not fully utilize this information. Additionally, the prevalent usage of Operational Taxonomic Unit, or OTUs is not without its own issues and may lead to a reduction in annotation capability of the system. Hence a novel approach to taxonomic annotation is needed. Here we introduce a new taxonomic annotation pipeline, TaxaSE, which utilizes Shannon entropy to quantify evolutionary conservation within 16S rDNA sequences for enhanced taxonomic annotations. Furthermore, the system is capable of annotation of individual sequences in order to improve fine grain taxonomic annotations. We present both in-silico comparison of the new similarity metric with percentage identity, as well as comparison with the popular QIIME pipeline. The results demonstrate the new similarity metric achieves better performance especially at lower taxa levels. Furthermore, the pipeline is able to extract more fine grain taxonomic annotations compared to QIIME. These exhibit not only the effectiveness of the new pipeline but also highlight the need to shift away from both percentage identity and OTU based approaches for ecological projects.

Since Swanson proposed the Undiscovered Public Knowledge (UPK) model, there have been many approaches to uncover UPK by mining the biomedical literature. These earlier works, however, required substantial manual intervention to reduce the number of possible connections and are mainly applied to disease-effect relation. With the advancement in biomedical science, it has become imperative to extract and combine information from multiple disjoint researches, studies and articles to infer new hypotheses and expand knowledge.