Demo Data from EDBT 2026 / reviewers in the wild / expert
Hongseok Tae
dblp:87/6819
· DBLP profile ↗
4ranked-venue papers
4as first author
0since 2021 · last 2014
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 4 · 4 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
2 papers |
Bioinformatics and computational biology · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › genomics
genotyping |
0.2 | 1 | 2014 | Discretized Gaussian mixture for genotyping of microsatellite loci containing homopolymer runs · Bioinform. 2014 |
Bioinformatics and computational biology
microsatellite genotyping |
0.2 | 1 | 2014 | Discretized Gaussian mixture for genotyping of microsatellite loci containing homopolymer runs · Bioinform. 2014 |
Bioinformatics and computational biology › sequence analysis
read mapping |
0.2 | 1 | 2013 | ReviSTER: an automated pipeline to revise misaligned reads to simple tandem repeats · Bioinform. 2013 |
Bioinformatics and computational biology
sequence alignment |
0.2 | 1 | 2013 | ReviSTER: an automated pipeline to revise misaligned reads to simple tandem repeats · Bioinform. 2013 |
Methods — techniques the papers use, named apart from their topics
rules-based approach · 0.2discretized gaussian mixture model · 0.2local mapping reference reconstruction · 0.2local alignment · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2014 | Discretized Gaussian mixture for genotyping of microsatellite loci containing homopolymer runsabstractMOTIVATION: Inferring lengths of inherited microsatellite alleles with single base pair resolution from short sequence reads is challenging due to several sources of noise caused by the repetitive nature of microsatellites and the technologies used to generate raw sequence data. RESULTS: We have developed a program, GenoTan, using a discretized Gaussian mixture model combined with a rules-based approach to identify inherited variation of microsatellite loci from short sequence reads without paired-end information. It effectively distinguishes length variants from noise including insertion/deletion errors in homopolymer runs by addressing the bidirectional aspect of insertion and deletion errors in sequence reads. Here we first introduce a homopolymer decomposition method which estimates error bias toward insertion or deletion in homopolymer sequence runs. Combining these approaches, GenoTan was able to genotype 94.9% of microsatellite loci accurately from simulated data with 40x sequence coverage quickly while the other programs showed <90% correct calls for the same data and required 5∼30× more computational time than GenoTan. It also showed the highest true-positive rate for real data using mixed sequence data of two Drosophila inbred lines, which was a novel validation approach for genotyping. AVAILABILITY: GenoTan is open-source software available at http://genotan.sourceforge.net. Hongseok Tae, Dong-Yun Kim, John McCormick 0005, Robert E. Settlage, Harold R. Garner |
Bioinform. | 1 |
| 2013 | ReviSTER: an automated pipeline to revise misaligned reads to simple tandem repeatsabstractMOTIVATION: Simple tandem repeats are highly variable genetic elements and widespread in genomes of many organisms. Next-generation sequencing technologies have enabled a robust comparison of large numbers of simple tandem repeat loci; however, analysis of their variation using traditional sequence analysis approaches still remains limiting and problematic due to variants occurring in repeat sequences confusing alignment programs into mapping sequence reads to incorrect loci when the sequence reads are significantly different from the reference sequence. RESULTS: We have developed a program, ReviSTER, which is an automated pipeline using a 'local mapping reference reconstruction method' to revise mismapped or partially misaligned reads at simple tandem repeat loci. RevisSTER estimates alleles of repeat loci using a local alignment method and creates temporary local mapping reference sequences, and finally remaps reads to the local mapping references. Using this approach, ReviSTER was able to successfully revise reads misaligned to repeat loci from both simulated data and real data. AVAILABILITY: ReviSTER is open-source software available at http://revister.sourceforge.net. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Hongseok Tae, Kevin W. McMahon, Robert E. Settlage, Jasmin H. Bavarva, Harold R. Garner |
Bioinform. | 1 |
| 2012 | ESTclean: A Cleaning Tool for Next-Gen Transcriptome Shotgun SequencingabstractBACKGROUND: With the advent of next-generation sequencing (NGS) technologies, full cDNA shotgun sequencing has become a major approach in the study of transcriptomes, and several different protocols in 454 sequencing have been invented. As each protocol uses its own short DNA tags or adapters attached to the ends of cDNA fragments for labeling or sequencing, different contaminants may lead to mis-assembly and inaccurate sequence products. RESULTS: We have designed and implemented a new program for raw sequence cleaning in a graphical user interface and a batch script. The cleaning process consists of several modules including barcode trimming, sequencing adapter trimming, amplification primer trimming, poly-A tail trimming, vector screening and low quality region trimming. These modules can be combined based on various sequencing applications. CONCLUSIONS: ESTclean is a software package not only for cleaning cDNA sequences, but also for helping to develop sequencing protocols by providing summary tables and figures for sequencing quality control in a graphical user interface. It outperforms in cleaning read sequences from complicated sequencing protocols which use barcodes and multiple amplification primers. Hongseok Tae, Dong-Sung Ryu, Suhas Sureshchandra, Jeong-Hyeon Choi |
BMC Bioinform. | 1 |
| 2007 | ASMPKS: an analysis system for modular polyketide synthasesabstractBACKGROUND: Polyketides are secondary metabolites of microorganisms with diverse biological activities, including pharmacological functions such as antibiotic, antitumor and agrochemical properties. Polyketides are synthesized by serialized reactions of a set of enzymes called polyketide synthase(PKS)s, which coordinate the elongation of carbon skeletons by the stepwise condensation of short carbon precursors. Due to their importance as drugs, the volume of data on polyketides is rapidly increasing and creating a need for computational analysis methods for efficient polyketide research. Moreover, the increasing use of genetic engineering to research new kinds of polyketides requires genome wide analysis. RESULTS: We describe a system named ASMPKS (Analysis System for Modular Polyketide Synthesis) for computational analysis of PKSs against genome sequences. It also provides overall management of information on modular PKS, including polyketide database construction, new PKS assembly, and chain visualization. ASMPKS operates on a web interface to construct the database and to analyze PKSs, allowing polyketide researchers to add their data to this database and to use it easily. In addition, the ASMPKS can predict functional modules for a protein sequence submitted by users, estimate the chemical composition of a polyketide synthesized from the modules, and display the carbon chain structure on the web interface. CONCLUSION: ASMPKS has powerful computation features to aid modular PKS research. As various factors, such as starter units and post-processing, are related to polyketide biosynthesis, ASMPKS will be improved through further development for study of the factors. Hongseok Tae, Eun-Bae Kong, Kiejung Park |
BMC Bioinform. | 1 |