• Genomics & Informatics
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• v.20 no.3
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• pp.36.1-36.6
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• 2022

BLAST, a basic bioinformatics tool for searching local sequence similarity, has been one of the most widely used bioinformatics programs since its introduction in 1990. Users generally use the web-based NCBI-BLAST program for BLAST analysis. However, users with large sequence data are often faced with a problem of upload size limitation while using the web-based BLAST program. This proves inconvenient as scientists often want to run BLAST on their own data, such as transcriptome or whole genome sequences. To overcome this issue, we developed NBLAST, a graphical user interface-based BLAST program that employs a two-way system, allowing the use of input sequences either as "query" or "target" in the BLAST analysis. NBLAST is also equipped with a dot plot viewer, thus allowing researchers to create custom database for BLAST and run a dot plot similarity analysis within a single program. It is available to access to the NBLAST with http://nbitglobal.com/nblast.

• Journal of Information Processing Systems
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• v.9 no.1
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• pp.31-40
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• 2013

In this paper, a novel method is proposed to build an ensemble of classifiers by using a feature selection schema. The feature selection schema identifies the best feature sets that affect the arrhythmia classification. Firstly, a number of feature subsets are extracted by applying the feature selection schema to the original dataset. Then classification models are built by using the each feature subset. Finally, we combine the classification models by adopting a voting approach to form a classification ensemble. The voting approach in our method involves both classification error rate and feature selection rate to calculate the score of the each classifier in the ensemble. In our method, the feature selection rate depends on the extracting order of the feature subsets. In the experiment, we applied our method to arrhythmia dataset and generated three top disjointed feature sets. We then built three classifiers based on the top-three feature subsets and formed the classifier ensemble by using the voting approach. Our method can improve the classification accuracy in high dimensional dataset. The performance of each classifier and the performance of their ensemble were higher than the performance of the classifier that was based on whole feature space of the dataset. The classification performance was improved and a more stable classification model could be constructed with the proposed approach.

• Interdisciplinary Bio Central
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• v.1 no.1
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• pp.5.1-5.4
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• 2009

This article is an addendum to the authors’ previous article (Kim, J. et al. (2008) Plant Cell 20, 307-319). The instrumentation and software described in this article were used to analyze the circadian leaf movement in the previous article. Here, we provide detailed and practical information on the instrumentation and the software. The source code of the LMA program is freely available from the authors. The circadian clock regulates a wide range of cyclic physiological responses with a 24 hour period in most organisms. Rhythmic leaf movement in plants is a typical robust manifestation of rhythms controlled by the circadian clock and has been used to monitor endogenous circadian clock activity. Here, we introduce a relatively easy, inexpensive, and simple approach for measuring leaf movement circadian rhythms using a USB-based web camera, public domain software and a Leaf Movement Assay (LMA) program. The LMA program is a semi-automated tool that enables the user to measure leaf lengths of individual Arabidopsis seedlings from a set of time-series images and generates a wave-form output for leaf rhythm. This is a useful and convenient tool for monitoring the status of a plant's circadian clock without an expensive commercial instrumentation and software.

• Bioinformatics and Biosystems
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• v.1 no.2
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• pp.99-102
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• 2006

The Basic Local Alignment Search Tool (BLAST) is one of the most established software in bioinformatics research and it compares a query sequence against the libraries of known sequences in order to investigate sequence similarity. Expressed Sequence Tags (ESTs) are single-pass sequence reads from mRNA (or cDNA) and represent the expression for a given cDNA library and the snapshot of genes expressed in a given tissue and/or at a given developmental stage. Therefore, ESTs can be very valuable information for functional genomics and bioinformatics researches. Although major bio database (DB) websites including NCBI are providing BLAST services and EST data, local DB and search system is demanding for better performance and security issue. Here we present animal EST DBs and local BLAST search system. The animal ESTs DB in NCBI Genbank were divided by animal species using the Perl script we developed. and we also built the new extended DB search systems fur the new data (Local Animal BLAST Search System: http://bioinfo.kohost.net), which was constructed on the high-capacity PC Cluster system fur the best performance. The new local DB contains 650,046 sequences for Bos taurus(cattle), 368,120 sequences for Sus scrofa (pig), 693,005 sequences for Gallus gallus (fowl), respectively.

• Molecules and Cells
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• v.44 no.11
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• pp.843-850
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• 2021

The rapid increase in collateral omics and phenotypic data has enabled data-driven studies for the fast discovery of cancer targets and biomarkers. Thus, it is necessary to develop convenient tools for general oncologists and cancer scientists to carry out customized data mining without computational expertise. For this purpose, we developed innovative software that enables user-driven analyses assisted by knowledge-based smart systems. Publicly available data on mutations, gene expression, patient survival, immune score, drug screening and RNAi screening were integrated from the TCGA, GDSC, CCLE, NCI, and DepMap databases. The optimal selection of samples and other filtering options were guided by the smart function of the software for data mining and visualization on Kaplan-Meier plots, box plots and scatter plots of publication quality. We implemented unique algorithms for both data mining and visualization, thus simplifying and accelerating user-driven discovery activities on large multiomics datasets. The present Q-omics software program (v0.95) is available at http://qomics.sookmyung.ac.kr.

• Genomics & Informatics
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• v.7 no.1
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• pp.46-48
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• 2009

Finishing is the most time-consuming step in sequencing, and many genome projects are left unfinished due to complex repeat regions. Here, we have developed BACContigEditor, a prototype shotgun sequence finishing tool. It is essentially an editor that visualizes assemblies of shotgun sequence fragment reads as gapped multiple alignments. The program offers some flexibility that is needed to rapidly resolve complex regions within a working session. The sole purpose of the release is to promote collaborative creation of extensible software for fragment assembly editors, foster collaborative development, and reduce barriers to initial tool development effort. We describe our software architecture and identify current challenges. The program is available under an Open Source license.

• Genomics & Informatics
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• v.20 no.1
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• pp.13.1-13.4
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• 2022

A major issue in the use of immune checkpoint inhibitors is their lack of efficacy in many patients. Previous studies have reported that the T cell inflamed signature can help predict the response to immunotherapy. Thus, many studies have investigated mechanisms of immunotherapy resistance by defining the tumor microenvironment based on T cell inflamed and non-T cell inflamed subsets. Although methods of calculating T cell inflamed subsets have been developed, valid screening tools for distinguishing T cell inflamed from non-T cell inflamed subsets using gene expression data are still needed, since general researchers who are unfamiliar with the details of the equations can experience difficulties using extant scoring formulas to conduct analyses. Thus, we introduce TcellInflamedDetector, an R package for distinguishing T cell inflamed from non-T cell inflamed samples using cancer gene expression data via bulk RNA sequencing.

• Biotechnology and Bioprocess Engineering:BBE
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• v.10 no.5
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• pp.425-431
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• 2005

The systems-level analysis of microbes with myriad of heterologous data generated by omics technologies has been applied to improve our understanding of cellular function and physiology and consequently to enhance production of various bioproducts. At the heart of this revolution resides in silico genome-scale metabolic model, In order to fully exploit the power of genome-scale model, a systematic approach employing user-friendly software is required. Metabolic flux analysis of genome-scale metabolic network is becoming widely employed to quantify the flux distribution and validate model-driven hypotheses. Here we describe the development of an upgraded MetaFluxNet which allows (1) construction of metabolic models connected to metabolic databases, (2) calculation of fluxes by metabolic flux analysis, (3) comparative flux analysis with flux-profile visualization, (4) the use of metabolic flux analysis markup language to enable models to be exchanged efficiently, and (5) the exporting of data from constraints-based flux analysis into various formats. MetaFluxNet also allows cellular physiology to be predicted and strategies for strain improvement to be developed from genome-based information on flux distributions. This integrated software environment promises to enhance our understanding on metabolic network at a whole organism level and to establish novel strategies for improving the properties of organisms for various biotechnological applications.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.7
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• pp.3053-3059
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• 2012

Human epidermal growth factor receptor 2 (HER2) is a member of the epidermal growth factor receptor family of receptor tyrosine kinases that plays important roles in all processes of cell development. Their overexpression is related to many cancers, including examples in the breast, ovaries and stomach. Anticancer therapies targeting the HER2 receptor have shown promise, and monoclonal antibodies against subdomains II and IV of the HER2 extra-cellular domain (ECD), Pertuzumab and Herceptin, are currently used in treatments for some types of breast cancers. Since anti HER2 antibodies targeting distinct epitopes have different biological effects on cancer cells; in this research linear and conformational B cell epitopes of HER2 ECD, subdomain III, were identified by bioinformatics analyses using a combination of linear B cell epitope prediction web servers such as ABCpred, BCPREDs, Bepired, Bcepred and Elliprro. Then, Discotope, CBtope and SUPERFICIAL software tools were employed for conformational B cell epitope prediction. In contrast to previously reported epitopes of HER2 ECD we predicted conformational B cell epitopes $P1_C$: 378-393 (PESFDGDPASNTAPLQ) and $P2_C$: 500-510 (PEDECVGEGLA) by the integrated strategy and P4: PESFDGD-X-TAPLQ; P5: PESFDGDP X TAPLQ; P6: ESFDGDP X NTAPLQP; P7: PESFDGDP-X-NTAPLQ; P8: ESFDG-XX-TAPLQPEQL and P9: ESFDGDP-X-NTAPLQP by SUPERFICIAL software. These epitopes could be further used as peptide antigens to actively immune mice for development of new monoclonal antibodies and peptide cancer vaccines that target different epitopes or structural domains of HER2 ECD.

• Journal of the Korea Society of Computer and Information
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• v.13 no.3
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• pp.65-75
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• 2008

Grid Computing has enabled enormous amount of computational jobs by connecting distributed computing resources. This technology has developed and widely used in various fields. Previous researches usually focused on how to efficiently manage and use the grid resources. However, there was not enough tries to understand and manage information of application softwares in a well-defined structure. Therefore users in application domain need to how about grid deeply to identify and describe the resource requirements matching for each jobs. We introduce a semantic grid management system based on application ontology to overcome this problem. We design and implement the ontology to store various information of the applications. With the ontology, this system can infer the resource requirements from input parameters and input data of the application software and automatically assign appropriate resources by matching the requirement. Also it can transform the information to other forms which grid middlewares can handle. We apply the system to construct an analysis environment of bioinformatics and compare it with other grid systems to explain usefulness of the system.