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- Creators: Can, Huansheng
- Status: Published
Description
I, Christopher Negrich, am the sole author of this paper, but the tools described were designed in collaboration with Andrew Hoetker. ConstrictR (constrictor) and ConstrictPy are an R package and python tool designed together. ConstrictPy implements the functions and methods defined in ConstrictR and applies data handling, data parsing, input/output (I/O), and a user interface to increase usability. ConstrictR implements a variety of common data analysis methods used for statistical and subnetwork analysis. The majority of these methods are inspired by Lionel Guidi's 2016 paper, Plankton networks driving carbon export in the oligotrophic ocean. Additional methods were added to expand functionality, usability, and applicability to different areas of data science. Both ConstrictR and ConstrictPy are currently publicly available and usable, however, they are both ongoing projects. ConstrictR is available at github.com/cnegrich and ConstrictPy is available at github.com/ahoetker. Currently, ConstrictR has implemented functions for descriptive statistics, correlation, covariance, rank, sparsity, and weighted correlation network analysis with clustering, centrality, profiling, error handling, and data parsing methods to be released soon. ConstrictPy has fully implemented and integrated the features in ConstrictR as well as created functions for I/O and conversion between pandas and R data frames with a full feature user interface to be released soon. Both ConstrictR and ConstrictPy are designed to work with minimal dependencies and maximum available information on the algorithms implemented. As a result, ConstrictR is only dependent on base R (v3.4.4) functions with no libraries imported. ConstrictPy is dependent upon only pandas, Rpy2, and ConstrictR. This was done to increase longevity and independence of these tools. Additionally, all mathematical information is documented alongside the code, increasing the available information on how these tools function. Although neither tool is in its final version, this paper documents the code, mathematics, and instructions for use, in addition to plans for future work, for of the current versions of ConstrictR (v0.0.1) and ConstrictPy (v0.0.1).
ContributorsNegrich, Christopher Alec (Author) / Can, Huansheng (Thesis director) / Hansford, Dianne (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This thesis research focuses on phylogenetic and functional studies of microbial communities in deep-sea water, an untapped reservoir of high metabolic and genetic diversity of microorganisms. The presence of photosynthetic cyanobacteria and diatoms is an interesting and unexpected discovery during a 16S ribosomal rRNA-based community structure analyses for microbial communities in the deep-sea water of the Pacific Ocean. Both RT-PCR and qRT-PCR approaches were employed to detect expression of the genes involved in photosynthesis of photoautotrophic organisms. Positive results were obtained and further proved the functional activity of these detected photosynthetic microbes in the deep-sea. Metagenomic and metatranscriptomic data was obtained, integrated, and analyzed from deep-sea microbial communities, including both prokaryotes and eukaryotes, from four different deep-sea sites ranging from the mesopelagic to the pelagic ocean. The RNA/DNA ratio was employed as an index to show the strength of metabolic activity of deep-sea microbes. These taxonomic and functional analyses of deep-sea microbial communities revealed a `defensive' life style of microbial communities living in the deep-sea water. Pseudoalteromonas sp.WG07 was subjected to transcriptomic analysis by application of RNA-Seq technology through the transcriptomic annotation using the genomes of closely related surface-water strain Pseudoalteromonas haloplanktis TAC125 and sediment strain Pseudoalteromonas sp. SM9913. The transcriptome survey and related functional analysis of WG07 revealed unique features different from TAC125 and SM9913 and provided clues as to how it adapted to its environmental niche. Also, a comparative transcriptomic analysis of WG07 revealed transcriptome changes between its exponential and stationary growing phases.
ContributorsWu, Jieying (Author) / Meldrum, Deirdre R. (Thesis advisor) / Zhang, Weiwen (Committee member) / Abbaszadegan, Morteza (Committee member) / Neuer, Susanne (Committee member) / Arizona State University (Publisher)
Created2013