2020
Sun, Jiao; Chang, Jae-Woong; Zhang, Teng; Yong, Jeongsik; Kuang, Rui; Zhang, Wei
Platform-integrated mRNA Isoform Quantification Journal Article
In: Bioinformatics, vol. 36, no. 8, pp. 2466–2473, 2020.
Links | BibTeX | Tags: Isoform Quantification
@article{WeiZhang2020,
title = {Platform-integrated mRNA Isoform Quantification},
author = {Jiao Sun and Jae-Woong Chang and Teng Zhang and Jeongsik Yong and Rui Kuang and Wei Zhang
},
url = {https://academic.oup.com/bioinformatics/article-abstract/36/8/2466/5675495?redirectedFrom=fulltext},
year = {2020},
date = {2020-04-15},
journal = {Bioinformatics},
volume = {36},
number = {8},
pages = {2466–2473},
keywords = {Isoform Quantification},
pubstate = {published},
tppubtype = {article}
}
Zhang, Wei; Petegrosso, Raphael; Chang, Jae-Woong; Sun, Jiao; Yong, Jeongsik; Chien, Jeremy; Kuang, Rui
A large-scale comparative study of isoform expressions measured on four platforms Journal Article
In: BMC Bioinformatics, vol. 21, no. 272, 2020.
Links | BibTeX | Tags: Isoform Quantification
@article{nanostring,
title = {A large-scale comparative study of isoform expressions measured on four platforms},
author = {Wei Zhang and Raphael Petegrosso and Jae-Woong Chang and Jiao Sun and Jeongsik Yong and Jeremy Chien and Rui Kuang},
url = {https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-020-6643-8},
year = {2020},
date = {2020-03-30},
journal = {BMC Bioinformatics},
volume = {21},
number = {272},
keywords = {Isoform Quantification},
pubstate = {published},
tppubtype = {article}
}
2015
Zhang, Wei; Chang, Jae-Woong; Lin, Lilong; Minn, Kay; Wu, Baolin; Chien, Jeremy; Yong, Jeongsik; Zheng, Hui; Kuang, Rui
Network-based Isoform Quantification with RNA-Seq Data for Cancer Transcriptome Analysis Journal Article
In: PLoS Computational Biology, vol. e1004465, 2015.
Abstract | Links | BibTeX | Tags: Isoform Quantification
@article{Net-RSTQ,
title = {Network-based Isoform Quantification with RNA-Seq Data for Cancer Transcriptome Analysis},
author = {Wei Zhang and Jae-Woong Chang and Lilong Lin and Kay Minn and Baolin Wu and Jeremy Chien and Jeongsik Yong and Hui Zheng and Rui Kuang},
url = {http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004465},
doi = {http://dx.doi.org/10.1371/journal.pcbi.1004465},
year = {2015},
date = {2015-12-23},
journal = {PLoS Computational Biology},
volume = {e1004465},
abstract = {New sequencing technologies for transcriptome-wide profiling of RNAs have greatly promoted the interest in isoform-based functional characterizations of a cellular system. Elucidation of gene expressions at the isoform resolution could lead to new molecular mechanisms such as gene-regulations and alternative splicings, and potentially better molecular signals for phenotype predictions. However, it could be overly optimistic to derive the proportion of the isoforms of a gene solely based on short read alignments. Inherently, systematical sampling biases from RNA library preparation and ambiguity of read origins in overlapping isoforms pose a problem in reliability. The work in this paper exams the possibility of using protein domain-domain interactions as prior knowledge in isoform transcript quantification. We first made the observation that protein domain-domain interactions positively correlate with isoform co-expressions in TCGA data and then designed a probabilistic EM approach to integrate domain-domain interactions with short read alignments for estimation of isoform proportions. Validated by qRT-PCR experiments on three cell lines, simulations and classifications of TCGA patient samples in several cancer types, Net-RSTQ is proven a useful tool for isoform-based analysis in functional genomes and systems biology.},
keywords = {Isoform Quantification},
pubstate = {published},
tppubtype = {article}
}
New sequencing technologies for transcriptome-wide profiling of RNAs have greatly promoted the interest in isoform-based functional characterizations of a cellular system. Elucidation of gene expressions at the isoform resolution could lead to new molecular mechanisms such as gene-regulations and alternative splicings, and potentially better molecular signals for phenotype predictions. However, it could be overly optimistic to derive the proportion of the isoforms of a gene solely based on short read alignments. Inherently, systematical sampling biases from RNA library preparation and ambiguity of read origins in overlapping isoforms pose a problem in reliability. The work in this paper exams the possibility of using protein domain-domain interactions as prior knowledge in isoform transcript quantification. We first made the observation that protein domain-domain interactions positively correlate with isoform co-expressions in TCGA data and then designed a probabilistic EM approach to integrate domain-domain interactions with short read alignments for estimation of isoform proportions. Validated by qRT-PCR experiments on three cell lines, simulations and classifications of TCGA patient samples in several cancer types, Net-RSTQ is proven a useful tool for isoform-based analysis in functional genomes and systems biology.