Finnish-British cooperation yields most accurate measures of gene expression

An RNA sequencing data analysis method called BitSeq, developed by Academy Research Fellow Antti Honkela’s research group together with researchers from the University of Manchester, has been found to be the most accurate gene transcript expression estimation method in a large international assessment. The method is based on probabilistic modelling, which can capture the uncertainty related to such measurements.

RNA sequencing can be used to measure the gene expression of humans and other organisms. The method has recently become very popular in bioscience and medical research, and it is currently being adopted for clinical applications. Compared to previous methods, RNA sequencing enables the study of alternative gene isoforms or transcripts, which are formed, for example, through the process of alternative splicing.

The analysis of the large amounts of data produced by RNA sequencing requires many advanced computational methods. Analysis of transcript-level data is especially demanding and the differences between alternative methods can be considerable.

In the recent international assessment, the BitSeq method developed at the University of Helsinki and the University of Manchester produced clearly the most reliable results in this task. In one subtask, it could produce equally accurate results using only half of the data needed by a very popular alternative method.

“The BitSeq method is based on probabilistic modelling, which allows us to compare different possible origins for observed sequences that cannot be identified uniquely. This enables computing probability distributions over the expression levels of each transcript of every gene in a way that captures the uncertainty and possible sources of error in the measurements. Accounting for this uncertainty through probabilities is essential for the accuracy of the method,” explains Antti Honkela.

The assessment article:

SEQC/MAQC-III Consortium. (2014). A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium. Nature Biotechnology. doi:10.1038/nbt.2957

Description of the BitSeq method:

Glaus, P., Honkela, A., & Rattray, M. (2012). Identifying differentially expressed transcripts from RNA-seq data with biological variation. Bioinformatics: 28(13), 1721–1728. doi:10.1093/bioinformatics/bts260

More information: Academy Research Fellow Antti Honkela, Helsinki Institute for Information Technology (HIIT), Department of Computer Science, University of Helsinki, tel. +358 50 311 2483,

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