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Information content and analysis methods for multi-modal high-throughput biomedical data.

ABSTRACT: The spectrum of modern molecular high-throughput assaying includes diverse technologies such as microarray gene expression, miRNA expression, proteomics, DNA methylation, among many others. Now that these technologies have matured and become increasingly accessible, the next frontier is to collect "multi-modal" data for the same set of subjects and conduct integrative, multi-level analyses. While multi-modal data does contain distinct biological information that can be useful for answering complex biology questions, its value for predicting clinical phenotypes and contributions of each type of input remain unknown. We obtained 47 datasets/predictive tasks that in total span over 9 data modalities and executed analytic experiments for predicting various clinical phenotypes and outcomes. First, we analyzed each modality separately using uni-modal approaches based on several state-of-the-art supervised classification and feature selection methods. Then, we applied integrative multi-modal classification techniques. We have found that gene expression is the most predictively informative modality. Other modalities such as protein expression, miRNA expression, and DNA methylation also provide highly predictive results, which are often statistically comparable but not superior to gene expression data. Integrative multi-modal analyses generally do not increase predictive signal compared to gene expression data.

SUBMITTER: Ray B

PROVIDER: S-EPMC3961740 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature

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Information content and analysis methods for multi-modal high-throughput biomedical data.

Ray Bisakha B Henaff Mikael M Ma Sisi S Efstathiadis Efstratios E Peskin Eric R ER Picone Marco M Poli Tito T Aliferis Constantin F CF Statnikov Alexander A

Scientific reports 20140321

The spectrum of modern molecular high-throughput assaying includes diverse technologies such as microarray gene expression, miRNA expression, proteomics, DNA methylation, among many others. Now that these technologies have matured and become increasingly accessible, the next frontier is to collect "multi-modal" data for the same set of subjects and conduct integrative, multi-level analyses. While multi-modal data does contain distinct biological information that can be useful for answering compl ...[more]

PMID: 24651673

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Project description:BackgroundDense genetic maps, together with the efficiency and accuracy of their construction, are integral to genetic studies and marker assisted selection for plant breeding. High-throughput multiplex markers that are robust and reproducible can contribute to both efficiency and accuracy. Multiplex markers are often dominant and so have low information content, this coupled with the pressure to find alternatives to radio-labelling, has led us to adapt the SSAP (sequence specific amplified polymorphism) marker method from a 33P labelling procedure to fluorescently tagged markers analysed from an automated ABI 3730 xl platform. This method is illustrated for multiplexed SSAP markers based on retrotransposon insertions of pea and is applicable for the rapid and efficient generation of markers from genomes where repetitive element sequence information is available for primer design. We cross-reference SSAP markers previously generated using the 33P manual PAGE system to fluorescent peaks, and use these high-throughput fluorescent SSAP markers for further genetic studies in Pisum.ResultsThe optimal conditions for the fluorescent-labelling method used a triplex set of primers in the PCR. These included a fluorescently labelled specific primer together with its unlabelled counterpart, plus an adapter-based primer with two bases of selection on the 3' end. The introduction of the unlabelled specific primer helped to optimise the fluorescent signal across the range of fragment sizes expected, and eliminated the need for extensive dilutions of PCR amplicons. The software (GeneMarker Version 1.6) used for the high-throughput data analysis provided an assessment of amplicon size in nucleotides, peak areas and fluorescence intensity in a table format, so providing additional information content for each marker. The method has been tested in a small-scale study with 12 pea accessions resulting in 467 polymorphic fluorescent SSAP markers of which 260 were identified as having been mapped previously using the radio-labelling technique. Heterozygous individuals from pea cultivar crosses were identifiable after peak area data analysis using the fluorescent SSAP method.ConclusionAs well as developing a rapid, and high-throughput marker method for genetic studies, the fluorescent SSAP system improved the accuracy of amplicon scoring, increased the available marker number, improved allele discrimination, and was sensitive enough to identify heterozygous loci in F1 and F2 progeny, indicating the potential to develop high-throughput codominant SSAPs.

Project description:Recently, rapid improvements in technology and decrease in sequencing costs have made RNA-Seq a widely used technique to quantify gene expression levels. Various normalization approaches have been proposed, owing to the importance of normalization in the analysis of RNA-Seq data. A comparison of recently proposed normalization methods is required to generate suitable guidelines for the selection of the most appropriate approach for future experiments.In this paper, we compared eight non-abundance (RC, UQ, Med, TMM, DESeq, Q, RPKM, and ERPKM) and two abundance estimation normalization methods (RSEM and Sailfish). The experiments were based on real Illumina high-throughput RNA-Seq of 35- and 76-nucleotide sequences produced in the MAQC project and simulation reads. Reads were mapped with human genome obtained from UCSC Genome Browser Database. For precise evaluation, we investigated Spearman correlation between the normalization results from RNA-Seq and MAQC qRT-PCR values for 996 genes. Based on this work, we showed that out of the eight non-abundance estimation normalization methods, RC, UQ, Med, TMM, DESeq, and Q gave similar normalization results for all data sets. For RNA-Seq of a 35-nucleotide sequence, RPKM showed the highest correlation results, but for RNA-Seq of a 76-nucleotide sequence, least correlation was observed than the other methods. ERPKM did not improve results than RPKM. Between two abundance estimation normalization methods, for RNA-Seq of a 35-nucleotide sequence, higher correlation was obtained with Sailfish than that with RSEM, which was better than without using abundance estimation methods. However, for RNA-Seq of a 76-nucleotide sequence, the results achieved by RSEM were similar to without applying abundance estimation methods, and were much better than with Sailfish. Furthermore, we found that adding a poly-A tail increased alignment numbers, but did not improve normalization results.Spearman correlation analysis revealed that RC, UQ, Med, TMM, DESeq, and Q did not noticeably improve gene expression normalization, regardless of read length. Other normalization methods were more efficient when alignment accuracy was low; Sailfish with RPKM gave the best normalization results. When alignment accuracy was high, RC was sufficient for gene expression calculation. And we suggest ignoring poly-A tail during differential gene expression analysis.

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Information content and analysis methods for multi-modal high-throughput biomedical data.

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Information content and analysis methods for multi-modal high-throughput biomedical data.

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