- TBC: A Clustering Algorithm Based on Prokaryotic Taxonomy
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Jae-Hak Lee , Hana Yi , Yoon-Seong Jeon , Sungho Won , Jongsik Chun
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J. Microbiol. 2012;50(2):181-185. Published online April 27, 2012
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DOI: https://doi.org/10.1007/s12275-012-1214-6
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High-throughput DNA sequencing technologies have revolutionized
the study of microbial ecology. Massive sequencing
of PCR amplicons of the 16S rRNA gene has been
widely used to understand the microbial community structure
of a variety of environmental samples. The resulting
sequencing reads are clustered into operational taxonomic
units that are then used to calculate various statistical indices
that represent the degree of species diversity in a given
sample. Several algorithms have been developed to perform
this task, but they tend to produce different outcomes.
Herein, we propose a novel sequence clustering algorithm,
namely Taxonomy-Based Clustering (TBC). This algorithm
incorporates the basic concept of prokaryotic taxonomy in
which only comparisons to the type strain are made and used
to form species while omitting full-scale multiple sequence
alignment. The clustering quality of the proposed method was
compared with those of MOTHUR, BLASTClust, ESPRITTree,
CD-HIT, and UCLUST. A comprehensive comparison
using three different experimental datasets produced by
pyrosequencing demonstrated that the clustering obtained
using TBC is comparable to those obtained using MOTHUR
and ESPRIT-Tree and is computationally efficient. The program
was written in JAVA and is available from http://sw.
ezbiocloud.net/tbc.
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Ok-Sun Kim, Namyi Chae, Hyun Soo Lim, Ahnna Cho, Jeong Hoon Kim, Soon Gyu Hong, Jeongsu Oh Journal of Microbiology.2012; 50(6): 1081. CrossRef
- rRNASelector: A Computer Program for Selecting Ribosomal RNA Encoding Sequences from Metagenomic and Metatranscriptomic Shotgun Libraries
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Jae-Hak Lee , Hana Yi , Jongsik Chun
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J. Microbiol. 2011;49(4):689-691. Published online September 2, 2011
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DOI: https://doi.org/10.1007/s12275-011-1213-z
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665
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Metagenomic and metatranscriptomic shotgun sequencing techniques are gaining popularity as more cost-effective
next-generation sequencing technologies become commercially available. The initial stage of bioinformatic
analysis generally involves the identification of phylogenetic markers such as ribosomal RNA genes.
The sequencing reads that do not code for rRNA can then be used for protein-based analysis. Hidden Markov
model is a well-known method for pattern recognition. Hidden Markov models that are trained on well-curated
rRNA sequence databases have been successfully used to identify DNA sequence coding for rRNAs in prokaryotes.
Here, we introduce rRNASelector, which is a computer program for selecting rRNA genes from
massive metagenomic and metatranscriptomic sequences using hidden Markov models. The program successfully
identified prokaryotic 5S, 26S, and 23S rRNA genes from Roche 454 FLX Titanium-based metagenomic and
metatranscriptomic libraries. The rRNASelector program is available at http://sw.ezbiocloud.net/rrnaselector.
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- Assessment of Soil Fungal Communities Using Pyrosequencing
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Young Woon Lim , Byung Kwon Kim , Changmu Kim , Hack Sung Jung , Bong-Soo Kim , Jae-Hak Lee , Jongsik Chun
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J. Microbiol. 2010;48(3):284-289. Published online June 23, 2010
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DOI: https://doi.org/10.1007/s12275-010-9369-5
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Pyrosequencing, a non-electrophoretic method of DNA sequencing, was used to investigate the extensive fungal community in soils of three islands in the Yellow Sea of Korea, between Korea and China. Pyrosequencing was carried out on amplicons derived from the 5′ region of 18S rDNA. A total of 10,166 reads were obtained, with an average length of 103 bp. The maximum number of fungal phylotypes in soil predicted at 99% similarity was 3,334. The maximum numbers of phylotypes predicted at 97% and 95% similarities were 736 and 286, respectively. Through phylogenetic assignment using BLASTN, a total of 372 tentative taxa
were identified. The majority of true fungal sequences recovered in this study belonged to the Ascomycota (182 tentative taxa in 2,708 reads) and Basidiomycota (172 tentative taxa in 6,837 reads). The predominant species of Ascomycota detected have been described as lichen-forming fungi, litter/wood decomposers, plant
parasites, endophytes, and saprotrophs: Peltigera neopolydactyla (Lecanoromycetes), Paecilomyces sp. (Sordariomycetes), Phacopsis huuskonenii (Lecanoromycetes), and Raffaelea hennebertii (mitosporic Ascomycota). The majority of sequences in the Basidiomycota matched ectomycorrhizal and wood rotting fungi, including species of the Agaricales and Aphyllophorales, respectively. A high number of sequences in the Thelephorales, Boletales, Stereales, Hymenochaetales, and Ceratobasidiomycetes were also detected. By
applying high-throughput pyrosequencing, we observed a high diversity of soil fungi and found evidence that pyrosequencing is a reliable technique for investigating fungal communities in soils.
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- Rapid Phylogenetic Dissection of Prokaryotic Community Structure in Tidal Flat Using Pyrosequencing
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Bong-Soo Kim , Byung Kwon Kim , Jae-Hak Lee , Myungjin Kim , Young Woon Lim , Jongsik Chun
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J. Microbiol. 2008;46(4):357-363. Published online August 31, 2008
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DOI: https://doi.org/10.1007/s12275-008-0071-9
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Dissection of prokaryotic community structure is prerequisite to understand their ecological roles. Various methods are available for such a purpose which amplification and sequencing of 16S rRNA genes gained its popularity. However, conventional methods based on Sanger sequencing technique require cloning process prior to sequencing, and are expensive and labor-intensive. We investigated prokaryotic community structure in tidal flat sediments, Korea, using pyrosequencing and a subsequent automated bioinformatic pipeline for the rapid and accurate taxonomic assignment of each amplicon. The combination of pyrosequencing and bioinformatic analysis showed that bacterial and archaeal communities were more diverse than previously reported in clone library studies. Pyrosequencing analysis revealed 21 bacterial divisions and 37 candidate divisions. Proteobacteria was the most abundant division in the bacterial community, of which Gammaand Delta-Proteobacteria were the most abundant. Similarly, 4 archaeal divisions were found in tidal flat sediments. Euryarchaeota was the most abundant division in the archaeal sequences, which was further divided into 8 classes and 11 unclassified euryarchaeota groups. The system developed here provides a simple, in-depth and automated way of dissecting a prokaryotic community structure without extensive pretreatment such as cloning.
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