scholarly journals Global diversity and geography of planktonic marine fungi

2020 ◽  
Vol 63 (2) ◽  
pp. 121-139 ◽  
Author(s):  
Brandon T. Hassett ◽  
Tobias R. Vonnahme ◽  
Xuefeng Peng ◽  
E.B. Gareth Jones ◽  
Céline Heuzé

AbstractGrowing interest in understanding the relevance of marine fungi to food webs, biogeochemical cycling, and biological patterns necessitates establishing a context for interpreting future findings. To help establish this context, we summarize the diversity of cultured and observed marine planktonic fungi from across the world. While exploring this diversity, we discovered that only half of the known marine fungal species have a publicly available DNA locus, which we hypothesize will likely hinder accurate high-throughput sequencing classification in the future, as it does currently. Still, we reprocessed >600 high-throughput datasets and analyzed 4.9 × 109 sequences (4.8 × 109 shotgun metagenomic reads and 1.0 × 108 amplicon sequences) and found that every fungal phylum is represented in the global marine planktonic mycobiome; however, this mycobiome is generally predominated by three phyla: the Ascomycota, Basidiomycota, and Chytridiomycota. We hypothesize that these three clades are the most abundant due to a combination of evolutionary histories, as well as physical processes that aid in their dispersal. We found that environments with atypical salinity regimes (>5 standard deviations from the global mean: Red Sea, Baltic Sea, sea ice) hosted higher proportions of the Chytridiomycota, relative to open oceans that are dominated by Dikarya. The Baltic Sea and Mediterranean Sea had the highest fungal richness of all areas explored. An analysis of similarity identified significant differences between oceanographic regions. There were no latitudinal gradients of marine fungal richness and diversity observed. As more high-throughput sequencing data become available, expanding the collection of reference loci and genomes will be essential to understanding the ecology of marine fungi.

MycoKeys ◽  
2018 ◽  
Vol 39 ◽  
pp. 29-40 ◽  
Author(s):  
Sten Anslan ◽  
R. Henrik Nilsson ◽  
Christian Wurzbacher ◽  
Petr Baldrian ◽  
Leho Tedersoo ◽  
...  

Along with recent developments in high-throughput sequencing (HTS) technologies and thus fast accumulation of HTS data, there has been a growing need and interest for developing tools for HTS data processing and communication. In particular, a number of bioinformatics tools have been designed for analysing metabarcoding data, each with specific features, assumptions and outputs. To evaluate the potential effect of the application of different bioinformatics workflow on the results, we compared the performance of different analysis platforms on two contrasting high-throughput sequencing data sets. Our analysis revealed that the computation time, quality of error filtering and hence output of specific bioinformatics process largely depends on the platform used. Our results show that none of the bioinformatics workflows appears to perfectly filter out the accumulated errors and generate Operational Taxonomic Units, although PipeCraft, LotuS and PIPITS perform better than QIIME2 and Galaxy for the tested fungal amplicon dataset. We conclude that the output of each platform requires manual validation of the OTUs by examining the taxonomy assignment values.


Genomics ◽  
2017 ◽  
Vol 109 (2) ◽  
pp. 83-90 ◽  
Author(s):  
Yan Guo ◽  
Yulin Dai ◽  
Hui Yu ◽  
Shilin Zhao ◽  
David C. Samuels ◽  
...  

2014 ◽  
Author(s):  
Simon Anders ◽  
Paul Theodor Pyl ◽  
Wolfgang Huber

Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard work flows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data such as genomic coordinates, sequences, sequencing reads, alignments, gene model information, variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability: HTSeq is released as open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index, https://pypi.python.org/pypi/HTSeq


Author(s):  
Anthony Federico ◽  
Stefano Monti

Abstract Summary Geneset enrichment is a popular method for annotating high-throughput sequencing data. Existing tools fall short in providing the flexibility to tackle the varied challenges researchers face in such analyses, particularly when analyzing many signatures across multiple experiments. We present a comprehensive R package for geneset enrichment workflows that offers multiple enrichment, visualization, and sharing methods in addition to novel features such as hierarchical geneset analysis and built-in markdown reporting. hypeR is a one-stop solution to performing geneset enrichment for a wide audience and range of use cases. Availability and implementation The most recent version of the package is available at https://github.com/montilab/hypeR. Contact [email protected] or [email protected]


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