Browsing by Author "Lee, Chi-Ching"

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ChIPseek, a web-based analysis tool for ChIP data
(2014-06-30)
Chen, Ting-Wen
;
Li, Hsin-Pai
;
Lee, Chi-Ching
;
Gan, Ruei-Chi
;
Huang, Po-Jung
Background: Chromatin is a dynamic but highly regulated structure. DNA-binding proteins such as transcription factors, epigenetic and chromatin modifiers are responsible for regulating specific gene expression pattern and may result in different phenotypes. To reveal the identity of the proteins associated with the specific region on DNA, chromatin immunoprecipitation (ChIP) is the most widely used technique. ChIP assay followed by next generation sequencing (ChIP-seq) or microarray (ChIP-chip) is often used to study patterns of protein-binding profiles in different cell types and in cancer samples on a genome-wide scale. However, only a limited number of bioinformatics tools are available for ChIP datasets analysis. Results: We present ChIPseek, a web-based tool for ChIP data analysis providing summary statistics in graphs and offering several commonly demanded analyses. ChIPseek can provide statistical summary of the dataset including histogram of peak length distribution, histogram of distances to the nearest transcription start site (TSS), and pie chart (or bar chart) of genomic locations for users to have a comprehensive view on the dataset for further analysis. For examining the potential functions of peaks, ChIPseek provides peak annotation, visualization of peak genomic location, motif identification, sequence extraction, and comparison between datasets. Beyond that, ChIPseek also offers users the flexibility to filter peaks and re-analyze the filtered subset of peaks. ChIPseek supports 20 different genome assemblies for 12 model organisms including human, mouse, rat, worm, fly, frog, zebrafish, chicken, yeast, fission yeast, Arabidopsis, and rice. We use demo datasets to demonstrate the usage and intuitive user interface of ChIPseek. Conclusions: ChIPseek provides a user friendly interface for biologists to analyze large scale ChIP data without requiring any programing skills. All the results and figures produced by ChIPseek can be downloaded for further analysis. The analysis tools built into ChIPseek, especially the ones for selecting and examine a subset of peaks from ChIP data, provides invaluable helps for exploring the high through-put data from either ChIP-seq or ChIP-chip.
Construction and analysis of a plant non-specific lipid transfer protein database (nsLTPDB)
(2012-01-17)
Wang, Nai-Jyuan
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Lee, Chi-Ching
;
Cheng, Chao-Sheng
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Lo, Wei-Cheng
;
Yang, Ya-Fen
Background: Plant non-specific lipid transfer proteins (nsLTPs) are small and basic proteins. Recently, nsLTPs have been reported involved in many physiological functions such as mediating phospholipid transfer, participating in plant defence activity against bacterial and fungal pathogens, and enhancing cell wall extension in tobacco. However, the lipid transfer mechanism of nsLTPs is still unclear, and comprehensive information of nsLTPs is difficult to obtain. Methods: In this study, we identified 595 nsLTPs from 121 different species and constructed an nsLTPs database - nsLTPDB - which comprises the sequence information, structures, relevant literatures, and biological data of all plant nsLTPs http://nsltpdb.life.nthu.edu.tw/. Results: Meanwhile, bioinformatics and statistics methods were implemented to develop a classification method for nsLTPs based on the patterns of the eight highly-conserved cysteine residues, and to suggest strict Prosite-styled patterns for Type I and Type II nsLTPs. The pattern of Type I is C X-2 V X5-7 C [V, L, I] x Y [L, A, V] X8-13 CC x G X-12 D x [Q, K, R] X-2 CXC X16-21 P X-2 C X13-15C, and that of Type II is C X-4 L X-2 C X9-11 P [S, T] X-2 CC X-5 Q X2-4 C[L, F] C X-2 [A, L, I] x [D, N] P X10-12 [K, R] X4-5 C X3-4 P X0-2 C. Moreover, we referred the Prosite-styled patterns to the experimental mutagenesis data that previously established by our group, and found that the residues with higher conservation played an important role in the structural stability or lipid binding ability of nsLTPs. Conclusions: Taken together, this research has suggested potential residues that might be essential to modulate the structural and functional properties of plant nsLTPs. Finally, we proposed some biologically important sites of the nsLTPs, which are described by using a new Prosite-styled pattern that we defined.
GI-POP: A combinational annotation and genomic island prediction pipeline for ongoing microbial genome projects
(2013-04-10)
Lee, Chi-Ching
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Chen, Yi-Ping Phoebe
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Yao, Tzu-Jung
;
Ma, Cheng-Yu
;
Lo, Wei-Cheng
Sequencing of microbial genomes is important because of microbial-carrying antibiotic and pathogenetic activities. However, even with the help of new assembling software, finishing a whole genome is a time-consuming task. In most bacteria, pathogenetic or antibiotic genes are carried in genomic islands. Therefore, a quick genomic island (GI) prediction method is useful for ongoing sequencing genomes. In this work, we built a Web server called GI-POP (http://gipop.life.nthu.edu.tw) which integrates a sequence assembling tool, a functional annotation pipeline, and a high-performance GI predicting module, in a support vector machine (SVM)-based method called genomic island genomic profile scanning (GI-GPS). The draft genomes of the ongoing genome projects in contigs or scaffolds can be submitted to our Web server, and it provides the functional annotation and highly probable GI-predicting results. GI-POP is a comprehensive annotation Web server designed for ongoing genome project analysis. Researchers can perform annotation and obtain pre-analytic information include possible GIs, coding/non-coding sequences and functional analysis from their draft genomes. This pre-analytic system can provide useful information for finishing a genome sequencing project. (c) 2012 Elsevier B.V. All rights reserved.
PARRoT - a homology-based strategy to quantify and compare RNA-sequencing from non-model organisms
(2016-12-22)
Gan, Ruei-Chi
;
Chen, Ting-Wen
;
Wu, Timothy H.
;
Huang, Po-Jung
;
Lee, Chi-Ching
Background: Next-generation sequencing promises the de novo genomic and transcriptomic analysis of samples of interests. However, there are only a few organisms having reference genomic sequences and even fewer having well-defined or curated annotations. For transcriptome studies focusing on organisms lacking proper reference genomes, the common strategy is de novo assembly followed by functional annotation. However, things become even more complicated when multiple transcriptomes are compared. Results: Here, we propose a new analysis strategy and quantification methods for quantifying expression level which not only generate a virtual reference from sequencing data, but also provide comparisons between transcriptomes. First, all reads from the transcriptome datasets are pooled together for de novo assembly. The assembled contigs are searched against NCBI NR databases to find potential homolog sequences. Based on the searched result, a set of virtual transcripts are generated and served as a reference transcriptome. By using the same reference, normalized quantification values including RC (read counts), eRPKM (estimated RPKM) and eTPM (estimated TPM) can be obtained that are comparable across transcriptome datasets. In order to demonstrate the feasibility of our strategy, we implement it in the web service PARRoT. PARRoT stands for Pipeline for Analyzing RNA Reads of Transcriptomes. It analyzes gene expression profiles for two transcriptome sequencing datasets. For better understanding of the biological meaning from the comparison among transcriptomes, PARRoT further provides linkage between these virtual transcripts and their potential function through showing best hits in SwissProt, NR database, assigning GO terms. Our demo datasets showed that PARRoT can analyze two paired-end transcriptomic datasets of approximately 100 million reads within just three hours. Conclusions: In this study, we proposed and implemented a strategy to analyze transcriptomes from non-reference organisms which offers the opportunity to quantify and compare transcriptome profiles through a homolog based virtual transcriptome reference. By using the homolog based reference, our strategy effectively avoids the problems that may cause from inconsistencies among transcriptomes. This strategy will shed lights on the field of comparative genomics for non-model organism. We have implemented PARRoT as a web service which is freely available at http://parrot.cgu.edu.tw.
PARRoT - a homology-based strategy to quantify and compare RNA-sequencing from non-model organisms
(2016-12-22)
Gan, Ruei-Chi
;
Chen, Ting-Wen
;
Wu, Timothy H.
;
Huang, Po-Jung
;
Lee, Chi-Ching
Background: Next-generation sequencing promises the de novo genomic and transcriptomic analysis of samples of interests. However, there are only a few organisms having reference genomic sequences and even fewer having well-defined or curated annotations. For transcriptome studies focusing on organisms lacking proper reference genomes, the common strategy is de novo assembly followed by functional annotation. However, things become even more complicated when multiple transcriptomes are compared. Results: Here, we propose a new analysis strategy and quantification methods for quantifying expression level which not only generate a virtual reference from sequencing data, but also provide comparisons between transcriptomes. First, all reads from the transcriptome datasets are pooled together for de novo assembly. The assembled contigs are searched against NCBI NR databases to find potential homolog sequences. Based on the searched result, a set of virtual transcripts are generated and served as a reference transcriptome. By using the same reference, normalized quantification values including RC (read counts), eRPKM (estimated RPKM) and eTPM (estimated TPM) can be obtained that are comparable across transcriptome datasets. In order to demonstrate the feasibility of our strategy, we implement it in the web service PARRoT. PARRoT stands for Pipeline for Analyzing RNA Reads of Transcriptomes. It analyzes gene expression profiles for two transcriptome sequencing datasets. For better understanding of the biological meaning from the comparison among transcriptomes, PARRoT further provides linkage between these virtual transcripts and their potential function through showing best hits in SwissProt, NR database, assigning GO terms. Our demo datasets showed that PARRoT can analyze two paired-end transcriptomic datasets of approximately 100 million reads within just three hours. Conclusions: In this study, we proposed and implemented a strategy to analyze transcriptomes from non-reference organisms which offers the opportunity to quantify and compare transcriptome profiles through a homolog based virtual transcriptome reference. By using the homolog based reference, our strategy effectively avoids the problems that may cause from inconsistencies among transcriptomes. This strategy will shed lights on the field of comparative genomics for non-model organism. We have implemented PARRoT as a web service which is freely available at http://parrot.cgu.edu.tw.