Evidence map›Paper›PMID 29179677›Full record

ArticleBMC genomics2017

Robust transcriptional signatures for low-input RNA samples based on relative expression orderings.

Huaping Liu, Yawei Li, Jun He, Qingzhou Guan, Rou Chen, Haidan Yan, Weicheng Zheng, Kai Song, Hao Cai, You Guo and 2 more

Abstract read
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Article in BMC genomics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

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29citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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29 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

12 authors.

Huaping LiuDepartment of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China.
Yawei LiDepartment of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China.
Jun HeDepartment of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China.
Qingzhou GuanDepartment of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China.
Rou ChenDepartment of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China.
Haidan YanDepartment of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China.
Weicheng ZhengDepartment of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China.
Kai SongDepartment of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.
Hao CaiDepartment of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China.
You GuoDepartment of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China.
Xianlong WangDepartment of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China. wang.xianlong@139.com.
Zheng GuoDepartment of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350122, China. guoz@ems.hrbmu.edu.cn.

Funding

National Natural Science Foundation of China 21534008National Natural Science Foundation of China 81372213National Natural Science Foundation of China 81572935the Joint Technology Innovation Fund of Fujian Province 2016Y9044
6 · The paper itself

Abstract

backgroundIt is often difficult to obtain sufficient quantity of RNA molecules for gene expression profiling under many practical situations. Amplification from low-input samples may induce artificial signals.

resultsWe compared the expression measurements of low-input mRNA samples, from 25 pg to 1000 pg mRNA, which were amplified and profiled by Smart-seq, DP-seq and CEL-seq techniques using the Illumina HiSeq 2000 platform, with those of the paired high-input (50 ng) mRNA samples. Even with 1000 pg mRNA input, we found that thousands of genes had at least 2 folds-change of expression levels in the low-input samples compared with the corresponding paired high-input samples. Consequently, a transcriptional signature based on quantitative expression values and determined from high-input RNA samples cannot be applied to low-input samples, and vice versa. In contrast, the within-sample relative expression orderings (REOs) of approximately 90% of all the gene pairs in the high-input samples were maintained in the paired low-input samples with 1000 pg input mRNA molecules. Similar results were observed in the low-input total RNA samples amplified and profiled by the Whole-Genome DASL technique using the Illumina HumanRef-8 v3.0 platform. As a proof of principle, we developed REOs-based signatures from high-input RNA samples for discriminating cancer tissues and showed that they can be robustly applied to low-input RNA samples.

conclusionsREOs-based signatures determined from the high-input RNA samples can be robustly applied to samples profiled with the low-input RNA samples, as low as the 1000 pg and 250 pg input samples but no longer stable in samples with less than 250 pg RNA input to a certain degree.

Indexed as

Gene Expression ProfilingHumansMCF-7 CellsNeoplasmsOligonucleotide Array Sequence AnalysisSequence Analysis, RNATranscriptomeLow-input RNA samples - amplification artificial signals - relative expression orderings - transcriptional signatures

Identifiers

PMID29179677
PMCPMC5704640

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.