Applications and Challenges of Real-time Mobile DNA Analysis

The DNA sequencing is the process of identifying the exact order of nucleotides within a given DNA molecule. The new portable and relatively inexpensive DNA sequencers, such as Oxford Nanopore MinION, have the potential to move DNA sequencing outside of laboratory, leading to faster and more accessible DNA-based diagnostics. However, portable DNA sequencing and analysis are challenging for mobile systems, owing to high data throughputs and computationally intensive processing performed in environments with unreliable connectivity and power. In this paper, we provide an analysis of the challenges that mobile systems and mobile computing must address to maximize the potential of portable DNA sequencing, and in situ DNA analysis. We explain the DNA sequencing process and highlight the main differences between traditional and portable DNA sequencing in the context of the actual and envisioned applications. We look at the identified challenges from the perspective of both algorithms and systems design, showing the need for careful co-design

Amplikoni põhine metsamuldade bakterikoosluse analüüs

Väitekirja elektrooniline versioon ei sisalda publikatsiooneMuldade rikkalike mikroobikoosluste uurimist on siiani palju takistanud tõsiasi, et enamik mulla mikroobe on kultiveerimatud. Seda kitsaskohta aitab leevendada lähenemine nimega metagenoomika, mis tähistab uurimistööd otse keskkonnaproovidest eraldatud geneetilise materjaliga. Selliste andmete kasutamiseks on levinud meetodid, mille abil grupeeritakse (klasterdatakse) kogutud DNA järjestused ad-hoc taksonoomilistesse üksustesse nn. OTU-desse (Operational Taxonomic Unit). Nii võib OTU-desse klasterdatud järjestusi kasutades hinnata bakterikoosluste mitmekesisust ja liigilist koostist. Saadud OTU-de arvukuse numbreid annab kasutada mitmesugustes erinevates analüüsides kui asendajaid tavapärasematele taksonoomilistele üksustele. Niisama kiire, kui on olnud uute sekveneerimistehnoloogiate areng, on ka olnud uute tööriistade arvu kasv – viimase kümnendi jooksul on loodud hulk programme, mis on mõeldud eelpoolmainitud OTU-de moodustamiseks DNA järjestuste andmetest. Antud doktoritöö töö keskendub sellele, kuidas mõjutavad erinevad OTU loomise meetodid edasisi analüüse ning järeldusi. Selleks kasutati järjestusandmeid artiklist “Bacterial community structure and its relationship to soil physico-chemical characteristics in alder stands with different management histories” ning erinevaid OTU klasterdamise meetodeid. OTU-d loodi erinevate programmide abil (Mothur,CROP,UCLUST,Swarm) – seejärel viidi läbi koosluste mitmesugused statistilised analüüsid. OTU andmete analüüs andis üldjoontes samasuguseid tulemusi. Seda visualiseerivad hästi töös olevad joonised. OTU arvude ja mitmekesisusindeksi statistilised testid ei leidnud statistiliselt olulist erinevust eri klasterdusmeetodite vahel. Kasutatud klasterdamismeetoditest jäid parimaina silma paistma CROP ja UCLUST meetodid.Lisaks näitasid analüüsid ka osade statistiliste meetodite eeliseid teiste ees sedasorti OTU andmete käsitlemiselThe soil as a central agent in many ecological processes has received a lot of research attention from many different angles. The investigation of the rich microbiome of the soil has been slowed by the fact that most of the microbes are unculturable. This gap can be filled by the metagenomics which is a field that deals with genetic material directly acquired form environmental samples. The analysis of 16S rDNA data usually begins with the construction of operational taxonomicunits (OTUs): clusters of reads that differ by less than a fixed sequence dissimilarity threshold. Consequently, the obtained sample-by-OTU abundance table serves as the basis for further statistical and exploratory analysis. During the last decade, a plethora of tools based on different principles and having different computational requirements to perform aforementioned OTU clustering has been created. This work we take an interest in the differences of the final outcome of series of analyses when different OTU clustering methods are used and also have a comparision of these methods. We used the dataset published in “Bacterial community structure and its relationship to soil physico-chemical characteristics in alder stands with different management histories” and analysed it using different software packages for processing bioinformatics data: Mothur UCLUST, CROP, Swarm. The results of analyses were on the whole quite similar and comparable.The differences between OTU numbers and diversity indeces were statistically not significant. The CROP and UCLUST methods stood out by their quality and useability. The work also showed the practicality of robust statistical methods when working with OTU data

A Comparative Metagenomics Study on A Bioreactor System in Salinas, CA, The Salinas River Valley, and The Tijuana River Valley

Coastal environments are some of the most productive and valuable ecosystems in the world while also having some of the highest levels of non-point source pollution. Genetic analyses of bacteria have provided scientists with a better understanding of how pollution affects functional potential within the environment. This is done by evaluating the presence/absence of particular taxonomic classifications as well as genes and gene functional groups. This study aimed to use genetic information isolated from bacteria found in two coastal watersheds, as well as a field bioremediation system, to learn how bacterial diversity and taxonomic groups differ between locations, the potential of sampled environments to remediate pollutants, and which functional groups are significantly represented within different locations. Amplicon sequencing of the 16S rRNA gene as well as whole metagenome shotgun sequencing were performed using isolated DNA from sediment samples collected from sites in two coastal watersheds, the Salinas River Watershed and the Tijuana River Watershed. Amplicon sequencing showed significant differences in alpha and beta diversity within different location sites. Beta diversity was also observed to be significantly affected by various environmental variables within location sites. Whole metagenome shotgun sequencing produced 60 high-quality dereplicated metagenomically assembled genomes (MAGs). MAGs from two location sites were found to have all genes necessary to complete two functional KEGG pathways related to agricultural runoff reduction. Hierarchical clustering of sequences within the high-quality dereplicated MAGs was also observed revealing over and under representation of 7 and 19 Level-3 GO categories, respectively. The genetic properties of bacteria found within this study’s sampling locations provides local policymakers with information related to an ongoing bioremediation project as well as the function of the ecosystems that are vital for the regional and national economy

Microbial Functional Diversity and the Associated Biogeochemical Interactions Across Miami-Dade County, Florida Soils

Decomposition of soil organic matter by microbial processes results in carbon sequestration within soils and/or carbon loss via atmospheric emission of carbon dioxide and methane. Natural as well as anthropogenic factors have been documented to impact soil microbial diversity and the associated biogeochemical functions. The soil microbial communities co-inhabiting Miami-Dade County soils, Florida are under threat because of the ongoing restoration efforts in the adjoining Florida Everglades Parks, predicted climatic changes such as sea-level rise and high rainfall, as well as urbanization. Therefore, an improved understanding of the current microbial functional communities is essential to better assess the impact of soil communities when anthropogenic or climatic disturbances occur. The objectives of the current study were to characterize the biodiversity and distribution of: a) cellulose degrading microbial community, and b) methanogenic guilds responsible for producing the gas methane, across four different Miami-Dade County, Florida soil types using the high throughput technique of GeoChip 5.0 functional microarray. In addition, the influence of vegetation cover, organic content, soil moisture content, pH, and soil texture in shaping the soil functional microbial community was also investigated. The function of cellulose degradation was distributed across wide range of taxonomic lineages with the majority belonging to the bacterial groups of Actinobacteria, Firmicutes, Alphaproteobacteria, and Gammaproteobacteria, whereas Ascomycota and Basidiomycota were the only detected fungal phyla. The cellulolytic bacterial community correlated more with the vegetation cover while fungal groups showed influence of moisture and organic content as well as percent silt. Six out of the seven methanogenic orders, with the greatest numbers found in the Methanomicrobiales, Methanosarcinales, and Methanomassiliicoccales, were identified across all four soil types of Miami-Dade. The abundance of the mcrA gene sequences was significantly greater with respect to soil moisture content. Additionally, the recently classified order Methanomassiliicoccales was identified across all four soils, including soils with lower moisture content not thought to provide ideal redox conditions to support methanogens. The greater number of correlation network interactions amongst the methanogenic guilds in the Florida Everglades wetlands versus the urbanized Miami-Dade County soils depicted the impact of the historical drainage of the Florida Everglades on the methanogenic community. Overall, the current study characterized the biodiversity of cellulolytic and methanogenic organisms across dry and saturated soils of Miami-Dade County and demonstrated that microbial guilds were functionally redundant and were influenced to some extent by the soil abiotic factors. Also, results from network analyses provide a platform to assess the future impacts of disturbances on the microbial community

Metagenomics, Metatranscriptomics, and Metabolomics Approaches for Microbiome Analysis

Microbiomes are ubiquitous and are found in the ocean, the soil, and in/on other living organisms. Changes in the microbiome can impact the health of the environmental niche in which they reside. In order to learn more about these communities, different approaches based on data from mul-tiple omics have been pursued. Metagenomics produces a taxonomical profile of the sample, metatranscriptomics helps us to obtain a functional profile, and metabolomics completes the picture by determining which byproducts are being released into the environment. Although each approach provides valuable information separately, we show that, when combined, they paint a more comprehensive picture. We conclude with a review of network-based approaches as applied to integrative studies, which we believe holds the key to in-depth understanding of microbiomes

Metagenomic binning with assembly graph embeddings

MOTIVATION: Despite recent advancements in sequencing technologies and assembly methods, obtaining high-quality microbial genomes from metagenomic samples is still not a trivial task. Current metagenomic binners do not take full advantage of assembly graphs and are not optimized for long-read assemblies. Deep graph learning algorithms have been proposed in other fields to deal with complex graph data structures. The graph structure generated during the assembly process could be integrated with contig features to obtain better bins with deep learning. RESULTS: We propose GraphMB, which uses graph neural networks to incorporate the assembly graph into the binning process. We test GraphMB on long-read datasets of different complexities, and compare the performance with other binners in terms of the number of High Quality (HQ) genome bins obtained. With our approach, we were able to obtain unique bins on all real datasets, and obtain more bins on most datasets. In particular, we obtained on average 17.5% more HQ bins when compared with state-of-the-art binners and 13.7% when aggregating the results of our binner with the others. These results indicate that a deep learning model can integrate contig-specific and graph-structure information to improve metagenomic binning. AVAILABILITY AND IMPLEMENTATION: GraphMB is available from https://github.com/MicrobialDarkMatter/GraphMB. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online