If you simply order an RNA-Seq library prep kit without specifying your objective, you will most likely be getting the one for Whole Transcriptome Sequencing. However, if you are thinking of doing the former and having the latter at the same time, you will have to think again because you will be losing something by doing so.
Below you will find a table which shows how many reads are normally required for different sequencing projects.
It is based on the publications which have general consensus among researchers. Nevertheless, it may still not be a golden rule for every sequencing project. What I want to draw your attention to, however, is the relative difference between the Whole Transcriptome Sequencing and Expression Profiling Sequencing. Bioinformatics 30 3 2 Liu Y. Plos One 8 6 :e 3 Bentley, D. Accurate whole human genome sequencing using reversible terminator chemistry. Nature , 53—59 4 Rozowsky, J.
Nature Biotech. Gene expression profiling experiments that are looking for a quick snapshot of highly expressed genes may only need 5—25 million reads per sample. In these cases, researchers can pool multiple RNA-Seq samples into one lane of a sequencing run, which allows for high multiplexing of samples.
Experiments looking for a more global view of gene expression, and some information on alternative splicing, typically require 30—60 million reads per sample.
Experiments looking to get an in-depth view of the transcriptome, or to assemble new transcripts, may require — million reads. In these cases, researchers may need to sequence multiple samples across several high output sequencing lanes.
Targeted RNA expression requires fewer reads. This requirement varies significantly depending on the tissue type being sequenced. Illumina strongly recommends using the primary literature to determine how many reads are needed, with most applications ranging from 1—5 million reads per sample.
The total number of aligned non-rRNA fragments in these datasets ranged from million. B Comparison of V. The ability to reliably identify differentially expressed genes by RNA-Seq is affected by a variety of factors aside from total sequencing depth that can vary significantly from one experiment to another, including the number of biological replicates included and the variation between them, the average abundance of differentially expressed genes, and the magnitude of their differential expression under the conditions tested.
Specifically, these data were derived from V. Finally, the total number of non-rRNA fragments for these datasets was between 4 and 6 million, significantly less than in the EDL datasets.
Despite these numerous differences, the impact of reducing the number of fragments in the V. These included all 16 of the major V. While it is not possible to accurately simulate how changes in depth will affect RNA-Seq comparative gene expression analyses in all cases, our findings indicate that in diverse species and growth conditions and even with relatively low correlation between biological replicates, million fragments per sample enable a significant number of genes differentially expressed by 2-fold or more to be identified with high statistical significance.
Our findings suggest that million non-rRNA fragments are sufficient to detect all but a few of the most low expressed genes in diverse bacteria growing under a variety of conditions. Moreover, we found that when the number of non-rRNA fragments in E. We also found that when RNA-Seq data from biological replicates is available, differential expression of numerous genes can be detected with high statistical significance even when the number of fragments per sample is reduced to million.
The optimal sequencing depth for an RNA-Seq based study will vary considerably based on the scientific objective of that study. For applications requiring a comprehensive transcriptome profile, coverage exceeding 10 million fragments per sample may be needed, with the understanding that increasing depth can lead to detection of sequences that may not represent bona fide transcripts.
Alternatively, the number and diversity of growth conditions included in the analysis can be increased with the expectation that, while the number of reads per sample will be decreased, numerous transcripts whose abundance is low under one condition will be more highly expressed and thus easier to detect under another condition.
Thus, our findings suggest that for many RNA-Seq based studies in bacteria, the number of fragments needed to profile gene expression in a single rRNA-depleted sample isolated from a bacterial monoculture is far less than that produced in a single Illumina HiSeq lane.
Indeed, our findings suggest that at a certain point increased sequencing depth may actually be detrimental to the accurate mapping of biologically relevant transcripts, yielding reads that likely represent contaminants in the cDNA library or the products of spurious transcriptional events.
A HiSeq lane typically produces about million paired end reads under current run conditions. Thus, multiplexing samples per lane will yield the million reads per sample that are sufficient for most applications of bacterial RNA-Seq. Indeed, our findings suggest that for studies of differential gene expression, even significantly higher levels of multiplexing result in relatively modest decreases in sensitivity.
Our findings also suggest that for studies in which only a few samples are to be sequenced in a single lane, a sufficient number of reads may be obtained for samples that are not depleted of rRNA and thus the time and cost associated with rRNA-depletion may not be justified.
Finally, for studies involving only one or two samples, such as pilot or proof-of-principle experiments, lower throughput platforms such as Illumina MiSeq platform may be more appropriate than the HiSeq platform. MiSeq yields only about 7. The analysis we conducted was largely limited to data derived from single bacterial strains grown in culture. Samples isolated from animal models are often contaminated with a large amount of host RNA. In RNA derived from microbial communities, transcripts corresponding to particular strains of interest will often be greatly outnumbered by those expressed by the numerous other members of the community.
Thus, in RNA-Seq data representing mixed samples, the number of reads corresponding to transcripts of interest can be orders of magnitude lower than in data derived from a homogeneous bacterial culture. We have conducted a systematic analysis of how changes in sequencing depth influence the profiling and comparison of transcriptomes by RNA-Seq in diverse bacterial species and growth conditions.
Our findings provide a guide for determining the appropriate sequencing depth for a wide variety of RNA-Seq-based studies of bacterial gene expression. Isolation of M. Unless otherwise indicated, all reagents in this section were obtained from Invitrogen.
Each set of 4 reactions was then combined and purified using MinElute columns Qiagen. Purified libraries were profiled using the Agilent Bioanalyzer and sequenced using the Illumina Hi-Seq platform to yield b paired end reads. Gene annotations were obtained from RefSeq and Rfam [ 36 ]. The overall fragment coverage of genomic regions corresponding to features such as ORFs and rRNAs was conducted as described [ 3 ].
In calculating the number of fragments aligning to each feature, the paired-end strand-specific RNA-Seq reads were assigned to these features based on their overlapping genomic coordinates and strand orientation using a custom PERL script.
Counts of RNA-Seq fragments were computed for each feature based on the paired-read mappings. Fragments aligning to the DNA strand opposite from the transcribed orientation of corresponding annotated features were classified and counted as antisense.
In the minority of cases where only one read of a pair aligned to the genome, the entire fragment was assigned to the overlapping feature. Differentially expressed genes were identified using the feature-assigned fragment counts for each replicate as input to the DESeq software [ 32 ]. Genome sequence coverage by RNA-Seq alignments was computed using a custom PERL script, where the strand-specific nucleotide coverage C was incremented at each nucleotide position spanned by a read or across the range covered by the boundaries of an RNA-Seq fragment inferred from a pair of properly mated paired end reads.
Background subtraction assuming a given percent of genomic DNA contamination pctBkg was performed as follows. The total strand-specific coverage was computed by summing strand-specific nucleotide-level coverage Csum observed across the genome. The expected nucleotide-level coverage due to genomic DNA contamination Cbkg was computed as:.
The effective nucleotide-level background-subtracted coverage Ceff values were computed as follows:. Nat Biotechnol. Nucleic Acids Res. Cell Host Microbe. The caveat with traditional RNA-Seq is the resolution of individual cells and cellular subpopulations are lost.
Single-Cell RNA-Seq allows researchers to not only identify cellular subpopulations, but to fully interrogate them at the single-cell level within a heterogeneous sample.
Similar to Standard RNA-Seq, Ultra-Low Input RNA-Seq provides bulk expression analysis of the entire cell population; however, as the name implies, a very limited amount of starting material is used, as low as 10 pg or a few cells. How Single-Cell Sequencing Works. Generally, Iso-Seq is superior to Illumina approaches when qualitative endpoints are of interest, such as alternative splicing, alternative polyadenylation, genome annotation, and novel transcript detection.
For quantitative assessment e. Please note that we do not perform the immunoprecipitation step. Contact the team directly; our contact information is at the bottom of the page.
Yes, we can recommend the best data delivery option based on the platform and project details. Options include:. If your results are being shipped to you via hard drive, you can track its shipment using the tracking number provided to you in the delivery email sent from GENEWIZ.
Please submit a quote request to receive accurate pricing information, as the cost depends on the details of the project. Furthermore, Preferred and Express options include guaranteed turnaround time and dedicated project managers.
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