Cross-Sectional Variations in Structure and Function of Coral Reef Microbiome With Local Anthropogenic Impacts on the Kenyan Coast of the Indian Ocean

Coral reefs face an increased number of environmental threats from anthropomorphic climate change and pollution from agriculture, industries and sewage. Because environmental changes lead to their compositional and functional shifts, coral reef microbial communities can serve as indicators of ecosystem impacts through development of rapid and inexpensive molecular monitoring tools. Little is known about coral reef microbial communities of the Western Indian Ocean (WIO). We compared taxonomic and functional diversity of microbial communities inhabiting near-coral seawater and sediments from Kenyan reefs exposed to varying impacts of human activities. Over 19,000 species (bacterial, viral and archaeal combined) and 4,500 clusters of orthologous groups of proteins (COGs) were annotated. The coral reefs showed variations in the relative abundances of ecologically significant taxa, especially copiotrophic bacteria and coliphages, corresponding to the magnitude of the neighboring human impacts in the respective sites. Furthermore, the near-coral seawater and sediment metagenomes had an overrepresentation of COGs for functions related to adaptation to diverse environments. Malindi and Mombasa marine parks, the coral reef sites closest to densely populated settlements were significantly enriched with genes for functions suggestive of mitigation of environment perturbations including the capacity to reduce intracellular levels of environmental contaminants and repair of DNA damage. Our study is the first metagenomic assessment of WIO coral reef microbial diversity which provides a much-needed baseline for the region, and points to a potential area for future research toward establishing indicators of environmental perturbations

A dive into the coral microbiome

Coral reefs are one of the most diverse ecosystems on the planet, harbouring approximately twenty-five percent of the diverse eukaryotic life in the oceans, while also being important economically for millions of people worldwide. Despite their importance, coral reefs are menaced of a very steep decline due to pollution and anthropogenic climate change. In this thesis, we investigate the microbes that live close-by and inside coral reefs. It is believed that microbiomes, both environmental and coral-associated, play an important role in coral health, both by contributing to nutrient cycling, such as carbon and nitrogen fixation as well as photosynthesis, and by protecting the corals against environmental stressors such as pathogens. These microbiomes can be studied using targeted approaches, such as metabarcoding, or more general and powerful approaches, called metagenomics. Metagenomics is a relatively new field of study and the first part of this thesis focuses on method development for metagenomics. In paper I, we present InSilicoSeq, a software package to simulate metagenomic Illumina reads. InSilicoSeq is useful for testing new bioinformatics methods as well as benchmarking existing ones. In paper II and III, we study the composition of the coral microbiome from previously published studies, and the composition and function of the microbiome of the water and upper sediment layer from reefs of the Kenyan coast of the west Indian ocean. We define a putative coral core microbiome at the genus level and take a look at the metabolic pathways that may be active in the surrounding environment of the corals. While the coral core microbiome was largely dominated by one genus, Endozoicomonas, the surrounding environment showed great diversity both in taxonomy and in metabolism. We found evidence of antibiotics resistance in the water, which we hypothesise mainly comes from agriculture. We also publish a catalogue of putative expressed pathways and discovered 174 new bacteria in the water and sediment samples

Simulating Illumina Metagenomic Data with InSilicoSeq

supporting files for https://github.com/HadrienG/InSilicoSe

Simulating Illumina data with InSilicoSeq

<p>InSilicoSeq (or iss) is a sequencing simulator producing (relatively) realistic Illumina reads primarily intended for simulating metagenomic samples, although it can be used to produce sequencing data from a single genome.</p><p>InSilicoSeq is written in python, and use a kernel density estimation model to model the read quality of real sequencing data.</p><p><br></p><p>InSilicoSeq is available at https://github.com/HadrienG/InSilicoSeq</p

NBISweden/EMBLmyGFF3: EMBLmyGFF3-1.2

change log: Modify installation of the tool. Could be installed/uninstalled using pip fix issue #1 => sorting the features to be sure they are sorted in increasing order of their locations, no matter their strand. Add python scripts to test the examples Some logs are now compressed by default for a better reading + Add the parameter --uncompressed_log to be able to have the log in its whole

Bioinformatics_tutorials

Data and slides to use with https://hadrieng.github.io/tutorials

eBioKit/tutorials: First release of tutorials for eBioKit

First release of tutorials for eBioKi

MetLab: An In Silico Experimental Design, Simulation and Analysis Tool for Viral Metagenomics Studies.

Metagenomics, the sequence characterization of all genomes within a sample, is widely used as a virus discovery tool as well as a tool to study viral diversity of animals. Metagenomics can be considered to have three main steps; sample collection and preparation, sequencing and finally bioinformatics. Bioinformatic analysis of metagenomic datasets is in itself a complex process, involving few standardized methodologies, thereby hampering comparison of metagenomics studies between research groups. In this publication the new bioinformatics framework MetLab is presented, aimed at providing scientists with an integrated tool for experimental design and analysis of viral metagenomes. MetLab provides support in designing the metagenomics experiment by estimating the sequencing depth needed for the complete coverage of a species. This is achieved by applying a methodology to calculate the probability of coverage using an adaptation of Stevens' theorem. It also provides scientists with several pipelines aimed at simplifying the analysis of viral metagenomes, including; quality control, assembly and taxonomic binning. We also implement a tool for simulating metagenomics datasets from several sequencing platforms. The overall aim is to provide virologists with an easy to use tool for designing, simulating and analyzing viral metagenomes. The results presented here include a benchmark towards other existing software, with emphasis on detection of viruses as well as speed of applications. This is packaged, as comprehensive software, readily available for Linux and OSX users at https://github.com/norling/metlab