Expression plots are colored according to aligner. Note, the loci displayed for Mup20 and Mup-ps22 are 22, bp and bp in length, respectively. Looking specifically at the DEGs, we compared the length, number of exons, average exon length, and GC content of genes identified by both platforms, Illumina only, Ion Torrent only, and neither platform. While there were no differences in the majority of these metrics between these groups of DEGs, we did observe a trend in the GC content Additional file 18 : Figure S9.
Both platforms have known GC biases [ 1 , 29 , 30 ] which could be contributing to these platform-specific differences.
In addition to read counts, we also compared the biotypes of the various genes detected in these data Additional file 19 : Table S9. Among these platform-specific genes, the exact percentage of protein coding and pseudogenes varies substantially depending upon both the sequencing platform and the aligner. However, many of these genes while platform-specific in data generated from one aligner were detected by both platforms when considering all of the aligners together Fig.
Curiously, in many of these cases the choice of alignment algorithm had substantial effects on the depth of coverage for these platform-specific genes. Consider two representative examples of differentially expressed genes: Serpina3e-ps and Btg3. We detected Serpina3e-ps Fig.
Similarly, we detected Btg3 Fig. In both of these cases, our ability to detect these genes was dependent both on our choice of sequencing platform, as well as our choice of alignment algorithm. We detected Mup20 at very high expression levels using all combinations of platform and aligner Fig. This is expected, as the major urinary protein MUP genes are expressed at very high levels in the livers of male mice [ 31 ]. This is a particularly extreme example of the phenomenon we observed previously, where our selection of both alignment algorithm and platform determines whether a gene is detected.
Interestingly, we found drastically different levels of expression for both genes across all alignment algorithms Fig. It is possible this variability in coverage could be explained by the differing strategies the aligners use to declare read alignments as ambiguous.
However, the majority of aligners assigned few, if any, multimappers to Mup-ps22 in either platform. To test for this effect we aligned all reads from sample using all thirteen aligners. Taken together these observations provide further evidence that the choice of platform and aligner can affect our ability to resolve expression originating from different genomic loci.
Additionally, simulated RNA-Seq reads were generated from both of these genes. Mup20 expression was simulated at three times the level of Mup-ps This figure displays the number of uniquely-mapped top and multimapped bottom reads aligned to Mup20 or Mup-ps Again we observed differences between the alignment algorithms in their ability to map reads to each of these two genes.
Furthermore, the majority of aligners, including the three we used for the bulk of this analysis, were able to accurately align reads to the Mup-ps Taken together, these findings suggest that the differing coverage patterns of Mup20 and Mup-ps22 as well as the Serpina3 genes between the Illumina and Ion Torrent data is not simply a function of the aligner choice, but rather an interaction between both the aligner and the sequencing platform.
We found very high concordance between both of these technologies in terms of gene-level read counts, which is in agreement with the previous comparison studies [ 2 , 8 ].
Additionally, we detected similar sets of differentially expressed genes in both sequencing platforms, and ultimately both Illumina and Ion Torrent data led to identical biological conclusions at the pathway level. In short, our results suggest a researcher would write the same paper, regardless of platform choice. That being said, we did notice differences between the data from both platforms. These differences are comparable to those from previous studies using UHRR samples where biological variability was not even a factor [ 33 ].
It is likely the majority of these platform-specific DEGs are the results of the technical variability arising from differences in the library preparation and sequencing technologies of both platforms. These observations led to the surprising finding that there appears to be an interaction between alignment algorithm and platform that affects the ability to detect absolute and differential gene expression.
Others have noticed the impact of aligner choice on downstream analysis within a single sequencing platform [ 12 , 34 ]. Here not only do we see these effects as well, we also observe that the impact of aligner choice is different depending upon whether we are using data derived from Illumina or Ion Torrent.
Given that several of these aligners were developed prior to the introduction of the Ion Torrent platform, it is possible some of these interactions are due to the underlying assumptions of these algorithms, which are based largely on Illumina data.
As a result, it may be possible to reduce the effects of this interaction through careful tuning of the alignment algorithm parameters to optimize for Ion Torrent.
For both platforms, researchers already use different library preparation methods to study small RNAs and non-coding transcripts. Four hours after treatment, the mice were euthanized through carbon dioxide induced asphyxiation and liver samples were dissected and snap-frozen in liquid nitrogen.
All procedures were approved and carried out in accordance with the Institutional Animal Care and Use Committee of the University of Pennsylvania.
Following preparation, library qualities were assessed using a Bioanalyzer Libraries from all samples were pooled together and sequenced using an Illumina HiSeq bp paired-end reads. The library qualities were checked by running on a BioAnalyzer and the concentrations were determined from the analysis profiles.
Ten barcoded libraries were pooled together on an equimolar basis and run using three PIv3 chips on an Ion Torrent Proton using HiQ chemistry. We aligned fastq files from both platforms using STAR v2. Next, we used Bowtie2 to align all of these unmapped reads to the reference genome.
Lastly, we used custom perl scripts to merge the Bowtie2 alignments with the STAR alignment, replacing entries for the unaligned reads with the mapping information from Bowtie2. We mapped reads to the mm9 version of the reference genome downloaded from the UCSC genome browser [ 35 ] for all alignment algorithms. See the Additional file 1 : Supplementary Methods for the full commands we used for each step in the alignment.
PORT is an implementation of the read re-sampling approach for normalization proposed by Li and Tibshirani [ 37 ]. Next, PORT determines the input dataset with the fewest number of gene-mapping reads and re-samples all datasets to have the same number of reads, thus accounting for batch effects and differences in sequencing depth between samples.
In addition to normalization, we also used PORT to quantify the normalized, gene-level read counts for each of our datasets. For quantification, we used the gene models from the Ensembl v67 annotation. We performed the majority of our quantification and differential expression analyses of the PORT quantification results in R.
Before any other analyses, we filtered out all genes with low expression. Briefly, we only retained those genes with at least five mapped reads, in five of the ten total samples. We used this set of expression-filtered genes for the remainder of our analyses. Lastly, we accounted for multiple testing using a Benjamini-Hochberg correction [ 22 ], as implemented by the p. We also used the limma v3. All further analyses and visualization of the data were performed using custom R scripts.
For the purposes of enrichment tests, we used the list of all detected genes i. Aside from these changes, we used the default parameters for our IPA analyses. See Additional file 1 : Supplementary Methods for full details.
A tale of three next generation sequencing platforms: comparison of ion torrent, Pacific biosciences and Illumina MiSeq sequencers.
BMC Genomics. Comprehensive evaluation of AmpliSeq transcriptome, a novel targeted whole transcriptome RNA sequencing methodology for global gene expression analysis.
Performance comparison between rapid sequencing platforms for ultra-low coverage sequencing strategy. PLoS One. Performance comparison of benchtop high-throughput sequencing platforms. Nat Biotechnol. Performance comparison of Illumina and ion torrent next-generation sequencing platforms for 16S rRNA-based bacterial community profiling.
Appl Environ Microbiol. Translating RNA sequencing into clinical diagnostics: opportunities and challenges. Nat Rev Genet. Utilization of Benchtop next generation sequencing platforms ion torrent PGM and MiSeq in noninvasive prenatal testing for chromosome 21 Trisomy and testing of impact of in Silico and physical size selection on its analytical performance.
An optimized protocol for generation and analysis of ion proton sequencing reads for RNA-Seq. Universal reference RNA as a standard for microarray experiments. The MicroArray quality control MAQC project shows inter- and intraplatform reproducibility of gene expression measurements.
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