If samples are limited, these are great alternatives to RNA-seq. Ion Torrent Guidelines. Ion Proton The Ion Proton system is a fast short read sequencer that generates million reads in a 2.
During sequencing, the four bases A, T, G, and C are introduced one at a time during the run. A nucleotide complementary to the base on the template is incorporated into the growing genome strand by DNA polymerase. Then, signal processing software measures incorporation and filters out low-accuracy readings.
Westly, E. Technology Review. Rothberg, J. An integrated semiconductor device enabling non-optical genome sequencing. This is not to say that these methods are invalid for use with Ion Torrent data, but we wanted to avoid using methods that might make assumptions specific to one of the sequencing platforms. All combinations of alignment algorithm and platform discovered roughly — differentially expressed genes, with the Illumina data detecting — more DEGs than the Ion Torrent data Fig.
Focusing on those genes detected as differentially expressed by only one of the platforms, we found the majority were at the fringes of detectability owing to their low expression levels or small fold-changes Fig.
Thus the typical gene not in the intersection was just below our significance cutoff in one platform. We hypothesize that the majority of these platform-specific DEGs that are truly differential would likely be detected by both platforms with additional sequencing depth. To test this hypothesis, we randomly down-sampled our normalized GSNAP data from both platforms to various levels, repeated the Mann-Whitney DE analysis in each down-sampled dataset, and compared the agreement between the two platforms as a function of coverage depth see Additional file 1 : Supplementary Methods for full details.
Our down-sampling experiment showed that as read depth increases, so does the percentage of total DEGs identified by both platforms Additional file 10 : Figure S5 , which provides initial evidence in support of this hypothesis. While these increases in concordance may seem modest, this down-sampling experiment uses read depths at the lower end of the spectrum 6—12 million reads; 2-fold change in read depth for most RNA-seq experiments. We also repeated our DE analysis using the limma package [ 23 ] to assess how an algorithm specifically designed for expression data would perform in these two platforms.
These differences are likely due to the differing statistical power of the tests underlying these two methods. Interestingly, many DEGs identified as platform-specific using Mann-Whitney were identified in both platforms using limma. We continue to use the Mann-Whitney DE results for the remainder of this manuscript, for the reasons we outlined above it is agnostic to platform and alignment method.
Differential expression comparison agrees across platforms. Within each combination of platform and aligner, differentially-expressed genes DEGs were identified using a two-sided Mann-Whitney test, followed by a Benjamini-Hochberg BH correction for multiple testing. Within each aligner, genes are colored according to the platform in which they were identified as DEGs. The DEGs identified by both platforms were among those with the highest expression levels and largest fold-change values Fig.
The fold-change values for platform-specific DEGs tended to be larger for the platform in which they were detected, though they still showed strong, positive correlations between the two platforms. Thus, both platforms are equally capable of identifying the most significant differences in gene expression and are in good agreement at the level of DEG detection. The IPA tool uses a curated database of literature and experimental results to identify the pathways and biological functions enriched among a list of DEGs.
We collected lists of DEGs from every combination of platform and alignment algorithm and analyzed each list separately using IPA. Both platforms showed strong enrichment of pathways related to the inflammatory response, regardless of alignment algorithm Fig. These top pathways include granulocyte adhesion and diapedesis , hepatic cholestasis , as well as various interleukin signaling pathways.
Both platforms show good agreement among the top enriched pathways. Ingenuity Pathway Analysis was performed separately on the lists of DEGs identified by each combination of aligner and platform. This figure presents the top 6 canonical pathways with significant enrichment in each dataset ordered by enrichment p -value. While the majority of our analyses indicate a strong agreement between both platforms, we did observe that some genes are detected in a platform-specific manner.
Examining the data from each of the alignment algorithms separately, we found the STAR alignments yielded the most platform-specific genes in the Illumina data, while GSNAP yielded the most platform-specific genes in the Ion Torrent data Fig. Additionally, the bulk of these platform-specific genes are mapped by less than 10 reads Fig.
This suggests that the genes which are detected in a platform-specific manner are expressed at low levels. We hypothesize that increasing read depth or performing a replicate of this experiment would allow for detection of these genes in both platforms. These numbers are displayed for all three alignment algorithms. Detected genes are defined as those with at least 5 reads in 5 of the samples. 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. It can also help doctors pinpoint weaknesses within different cancers to create new treatments. Genome transcription can also be used to benefit otherwise healthy patients in a preventative manner.
So many health problems have their roots in our genes. For instance, some families may have tendencies toward heart disease, diabetes or even alcoholism.
Using specialized software that reads patterns in gene sequences, physicians could warn patients of their vulnerable areas. That's not to say genome sequencing can predict that a given patient will have a heart attack or become an alcoholic, but it can warn that person if he or she is at greater risk.
Then, they can make informed decisions to address that risk before it becomes a medical emergency. The same might be done for everything from Alzheimer's disease to bone density problems.
It stands to reason that, one day, most people could have a map of their personal genome saved somewhere within their electronic medical file. After all, health conditions will come and go, but DNA is a lifelong blueprint. As those health conditions arise, DNA can be checked to offer doctors a deeper understanding of their patients. That's why these sequencing tools will eventually start to migrate out of labs and into hospitals clinics.
That moment hasn't arrived yet. In fact, even though it's on the market, your doctor couldn't order a reading of your genome from the Proton or any of the other next-generation systems on the market as of July Keep reading and find out why. With this revolutionary technology unraveling the mysteries of our genes at such a rapid pace, it must be noted that all of Life Technologies' publicity materials for new-product launches featured the same statement buried somewhere within.
They say that "these products are for research use only, and not intended for any animal or human therapeutic or diagnostic use. You just can't study a specific person's genome for the sake of helping them make decisions about their health treatment. Since this is such a new technology, the U. Food and Drug Administration FDA must review and approve all of these next-generation sequencing machines before they're let loose on patients.
In the summer of , the FDA called a panel of manufacturers and experts to discuss the questions surrounding these kinds of sequencers. While everyone agreed that they are a powerful tool, many questions have to be addressed before they will be allowed to process human genetic information for the purpose of clinical decision-making.
They range from how the sample is extracted to ensure it's clean to how the software outputs and interprets its data. With many manufacturers approaching the same problem from different angles, the FDA has determined that it needs to devise a system for validating new sequencers before they are approved for clinical use.
The agency wants to give doctors assurance that the systems can guarantee a certain level of accuracy. The problem with that proposition comes with devising a standardized test for sequencers. It would be ideal if the FDA had a set of living cells that had already been sequenced and use them as the test bed.
The problem is that cells will tend to mutate over time, so getting that standardized field will be difficult [source: FDA ]. In the meantime, it will undoubtedly have an impact on the research community by allowing for faster, cheaper tests on everything from tumors to tree leaves. I love bragging to my friends about the titles of my assignments for HowStuffWorks.
Whatever you just said doesn't exist," was probably the best response I heard. It's subjects like the Proton that makes writing for this site so much fun. The Proton and its peers represent a giant leap in genetic technology.
The closest analogy is maybe computers in the late s and '80s. As soon as they became so affordable that average families could own them, suddenly everyone started getting computers. As far as labs are concerned, the Proton is the Apple MacIntosh of genetic technology; compact, easy to use, a revolutionary presence in the market, and it packs an entire lab worth of analytics into a tiny box.
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