Computational approaches for isoform detection and estimation: good and bad news

Abstract

BackgroundThe main goal of the whole transcriptome analysis is to correctly identify all expressed transcripts within a specific cell/tissue - at a particular stage and condition - to determine their structures and to measure their abundances. RNA-seq data promise to allow identification and quantification of transcriptome at unprecedented level of resolution, accuracy and low cost. Several computational methods have been proposed to achieve such purposes. However, it is still not clear which promises are already met and which challenges are still open and require further methodological developments.ResultsWe carried out a simulation study to assess the performance of 5 widely used tools, such as: CEM, Cufflinks, iReckon, RSEM, and SLIDE. All of them have been used with default parameters. In particular, we considered the effect of the following three different scenarios: the availability of complete annotation, incomplete annotation, and no annotation at all. Moreover, comparisons were carried out using the methods in three different modes of action. In the first mode, the methods were forced to only deal with those isoforms that are present in the annotation; in the second mode, they were allowed to detect novel isoforms using the annotation as guide; in the third mode, they were operating in fully data driven way (although with the support of the alignment on the reference genome). In the latter modality, precision and recall are quite poor. On the contrary, results are better with the support of the annotation, even though it is not complete. Finally, abundance estimation error often shows a very skewed distribution. The performance strongly depends on the true real abundance of the isoforms. Lowly (and sometimes also moderately) expressed isoforms are poorly detected and estimated. In particular, lowly expressed isoforms are identified mainly if they are provided in the original annotation as potential isoforms.ConclusionsBoth detection and quantification of all isoforms from RNA-seq data are still hard problems and they are affected by many factors. Overall, the performance significantly changes since it depends on the modes of action and on the type of available annotation. Results obtained using complete or partial annotation are able to detect most of the expressed isoforms, even though the number of false positives is often high. Fully data driven approaches require more attention, at least for complex eucaryotic genomes. Improvements are desirable especially for isoform quantification and for isoform detection with low abundance.

Assisted transcriptome reconstruction and splicing orthology

“…As shown in [2], the best-performing computational methods identified at most 21 % of spliced protein-coding transcripts from H. sapiens , and transcript detection remains low even with very high sequencing coverage, leading the authors to conclude that improved results would have to wait for technological advances. Those findings were confirmed by several other studies [3–5], that include methods recently developed such as StringTie [6]. …”

Comparative evaluation of full-length isoform quantification from RNA-Seq

“…So why did we embark on another comparative study? Angelini et al 8 and Kanitz et al 12 are four and five years old, respectively, and hence they do not reflect the recent developments in this fast-changing field. For instance, they do not include the popular pseudo-alignment-based method kallisto .…”

Benchmarking of the Oxford Nanopore MinION sequencing for quantitative and qualitative assessment of cDNA populations

“…Transcriptome sequencing using short read technologies (Illumina HiSeq 2500 1 , Ion Proton 2 ) provides valuable information on transcript abundance, rare transcripts and variable transcription start or end sites. Nevertheless, inferring alternatively spliced isoforms of genes from short read data through statistical assignment of the most probable combination of exons is still computationally challenging and not very accurate 3 . Uneven read coverage 4 , complex splicing 5 and potential sequencing bias 6 complicates even more the task.…”