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The statistical test results are summarized in Fig. Each point in the plot corresponds to a gene. The x-axis represents the log2 fold change of stranded versus non-stranded, while the y-axis -log10 Adjusted PValue corresponds to the significance of statistical test. A total of significant genes were identified to be differentially expressed DE and are colored in red in Fig. Of those significant genes, genes top right corner have higher expression in stranded than in non-stranded sequencing, while genes top left corner are down regulated, having lower expression in stranded than in non-stranded RNA-seq.
The large number of differential expression genes in Fig. Differential analysis results for the comparison between stranded and non-stranded RNA-seq. Every point in the plot corresponds to a gene. The x-axis represents the log2 fold change of stranded over non-stranded, while the y-axis -log10 AdjustedPValue corresponds to the significance of a statistical test.
All significant genes are colored in red. A gene is considered to be expressed if its maximal expression across all eight samples is greater than 1 CPM count per millionand accordingly, a total of 16, expressed genes survived this filtering. All genes that have appreciable expression and those DE genes in Fig. The detailed description of each gene category from Gencode annotation was described previously [ 25 ]. Thus, the differential expression we observe is associated with gene type.
Globally, To test whether the apparent enrichment of antisense genes and pseudogenes is statistically significant, the built-in binomial proportions test prop.
The calculated p values are smaller than 2. The association between differential expression and gene overlapping is gene-type dependent. It represents what percentage of genes is differentially expressed.
Therefore, the sum of the last four columns is equal to the total number of genes in that category. The association between differential expression and gene overlap is gene-type dependent. Antisense and pseudogene are enriched.
The y-axis represents percentage. Next, we explored the association between differential analysis results and sequencing protocol. Every gene dot in Fig. For pseudogenes, no apparent association is observed, and confirmed by statistical test. All tests report a P value lower than 2. The overwhelming majority of antisense DE genes show higher expression in stranded RNA-seq, and their expressions in non-stranded RNA-seq are quite often zero or very low.
Antisense transcripts can The Strand Of The Plot - Rename - Touch (Mixes) (CDr) as regulatory elements in the regulation of gene expression [ 12 ], and a number of antisense transcripts are related to various human disorders [ 26 ].
A proper elucidation of the antisense transcriptome and its quantification will reveal their novel function in regulation The Strand Of The Plot - Rename - Touch (Mixes) (CDr) gene expression. Based on these observations, we have shown that the stranded RNA-seq is more effective than non-stranded RNA-seq in properly quantifying expression for antisense genes.
The ENCODE project recently performed a survey of publicly available expression data to identify transcribed pseudogenes and found over pseudogenes with strong evidence of transcription [ 27 ]. Recent studies have shown that some pseudogenes are transcribed and contribute to cancer when dysregulated [ 28 ]. In particular, pseudogene transcripts can function as competing endogenous RNAs [ 29 ].
However, reliable quantification of pseudogene expression remains a challenging problem for a number of reasons. First, because parent genes and pseudogenes are highly similar in nucleotide sequence, short RNA-seq reads derived from one may align equally well to others. Such reads are fundamentally ambiguous in terms of their origin.
Second, some reads may have nearly identical alignment to locations in the gene and pseudogene, and their mapping is often determined by the location with the least error in alignment. This strategy is unreliable and can result in an incorrect assignment of the read [ 29 ]. The enrichment of pseudogenes in differential analysis in Fig.
Usually the expression level for pseudogenes is not high. For those DE pseudogenes, the average expression is 3. We speculated the enrichment for pseudogenes might arise from 1 the read mapping uncertainty in pseudogenes, 2 the lower expression levels for pseudogenes, and 3 the additional bias introduced by sequencing protocols. We checked the read mapping profiles for some pseudogenes unpublished resultsand found that quite often those reads that mapped to pseudogenes have mismatches.
Because of the intrinsic uncertainty in read mapping, we should be cautious about the gene quantification and differential analysis results for pseudogenes. For a given gene, if stranded and non-stranded RNA-seq report different expression levels, which one is more reliable? In principle, the stranded RNA-seq should be more accurate because additional information i. Below, we selected a few example genes i. The expressions for these three genes are shown in bar charts in Fig.
Interleukin IL 24 is a secreted protein of the IL10 family, and its expression has been identified in certain cell types. In vivoIL24 is predominantly expressed by skin tissue cells during inflammatory conditions, such as psoriasis [ 30 ]. The read mapping results in PFE1 are shown in Fig. All genes, transcripts, and sequence reads in Fig.
In non-stranded RNA-seq, all reads mapped to IL24 are counted, regardless of their originating genomic strand. As a result, those reads are not counted, clarifying why stranded RNA-seq reports no expression for IL The coverage pattern of sequence reads in Fig.
The uniformity bias in RNA-seq does not explain the extremely uneven coverage pattern observed in Fig. Moreover, this cytokine is not expected to have a high expression in whole blood RNA-seq [ 30 ]. In contrast, the result in stranded RNA-seq is more reliable, and biologically makes sense with previous observations. In non-stranded RNA-seq, all reads mapped to IL24 are counted regardless if they are in the forward or reverse strands.
However, the coverage pattern of sequence reads does not support the sequence reads mapped to the IL24 genomic region that truly originate from this gene. Because those reads in Fig. As we know, our current gene annotation is neither complete nor comprehensive, and it is likely that such reads originate from a novel gene at the opposite strand of IL We currently do not have a good explanation for these mapped reads.
However, the scenario in Fig. ICAM4 intercellular adhesion molecule 4 shows moderate expression in whole blood [ 31 ]. However, non-stranded RNA-seq reports no expression for this gene, and the reason is revealed in Fig.
It overlaps with another gene CTDP2. The ambiguous reads in overlapping regions are thus excluded from counting in FeatureCounts, and this explains the lack of expression for ICAM4 on non-stranded RNA-seq. The ambiguous reads in overlapping genes in Fig. According to our sequencing protocol, it is impossible for such reads to originate from CTDP2. The gene expression in stranded RNA-seq also agrees with other supporting evidence [ 31 ], and is again more reliable than in non-stranded RNA-seq.
In non-stranded RNA-seq, the ambiguous reads in overlapping regions are excluded from counting, which explains why there is no expression for ICAM4. However, the ambiguous reads can be perfectly resolved in stranded RNA-seq. For the scenario in non-stranded RNA-seq in Fig. Despite the fact that RSEM is capable of fully handling reads that map ambiguously or fall into the gene overlapping regions, it proportionally distributes ambiguous reads according to the number of unique reads in overlapping genes.
If a gene is completely contained within another gene, it has no unique read at all. As a consequence, zero reads are counted to that gene. According to the theoretical calculation above, there are a total of genes completely contained with other genes from opposite strands.
In short, the read ambiguity in non-stranded RNA-seq in Fig. GAPDH glyceraldehydephosphate dehydrogenase is a well-known housekeeping gene with very high expression in most cell types and tissues. The reason for this underestimation can be easily understood when considering the gene overlap shown in Additional file 1 : Figure S3.
In this paper, we performed a side-by-side comparison of stranded and non-stranded RNA-seq, and investigated the gene overlap both in our practical whole blood RNA-seq dataset and from the theoretical perspective. Our study demonstrates that stranded RNA-seq provides a more accurate estimate of transcript expression compared with non-stranded RNA-seq and is therefore the recommended RNA-seq approach for all future mRNA-seq studies.
We used various freely available open source tools and implemented an in-house pipeline for stranded and non-stranded RNA-seq data analyses Fig. The details on each step in the data generation and analyses are described below. Blood was pooled across subjects to create a single pooled sample. This pooled blood was dispensed into a set of approximately mL aliquots.
An aliquot of 1. Six hundred nanograms of RNA post-GlobinClear were divided into two ng aliquots, with one aliquot submitted to stranded RNA-seq processing and the second aliquot submitted to non-stranded RNA-seq processing. A minimum of 60 M reads were generated from each library. The human genome database and gene annotation database were used to map and count sequence reads.
The reads were mapped to the hg19 reference genome using STAR v2. The mapping was performed on the Pfizer High Performance Computing cluster. The mapping summaries, such as the percentage of reads that were uniquely mapped, multiple mapped, or unmapped, were then collected from the log files of STAR runs see Results.
To count reads mapped to individual genes in Gencode, the program featureCounts [ 16 ] was used. FeatureCounts assigns a read to a feature a gene or labels it as matching to no feature or as ambiguous if it matches more than one feature and it cannot determine which one it is.
Only uniquely mapped reads are used in the counting step. Like the mapping step above, the counting metrics were collected from the summary file of each featureCounts run.
Genes that have expression levels less than 1 CPM were labeled as low expressed. If a gene had zero or low expression across all eight samples, it was omitted from the correlation and differential expression analysis. This filtering step was included to reduce the false positives in the differential analysis [ 33 ]. A counts table was generated by featureCounts and then used for the DE analysis. The differential analysis was performed by R packages edgeR 3.
We compared the stranded versus non-stranded sequencing groups. All genes with a fold change greater than 1. The estimation was performed by R package GenomicFeatures 1.
We then extracted all exons from TxDb and grouped them by gene. According to strand information, the genes in each chromosome were divided into two groups. The overlaps at the same and opposite strands were quantified at both gene and nucleotide base levels see Fig. For each pair of overlapping genes, for example G1 and G2, the lengths for flattened exons were calculated and the short gene was selected for calculating the ratio of overlapping.
The histogram and cumulative distribution of overlap were quantified Fig. Written informed consent was obtained from all volunteer blood donors for the research described and potential publication thereof. A copy of the written consent is available for review by the Editor of this journal.
Samples from individuals were coded at the time of collection and then pooled prior to data generation, removing any possible association of analytical measurements with a single donor. The R script to estimate the gene overlap is attached as Additional file 1 : Script 1.
We received no funding support from any third party. Additional file 1:Table S1. Tables S2. Tabulate the overlapping summaries of Gencode V19 annotation database at both the gene and the nucleotide base levels, respectively.
Figures S1. Show all-against-all scatter plots of gene expression profile among RNA-seq samples sequenced by stranded and non-stranded protocols, respectively. Figure S3. Script 1. Contains the R script to estimate the gene overlap in Gencode Release PDF kb. Competing interests. SZ implemented the analysis pipeline, performed the data analysis and drafted the manuscript. All authors participated in the writing and review of the report, and all approved the final manuscript. Shanrong Zhao, Email: moc.
Ying Zhang, Email: moc. William Gordon, Email: moc. Jie Quan, Email: moc. Hualin Xi, Email: moc. Sarah Du, Email: moc. David The Strand Of The Plot - Rename - Touch (Mixes) (CDr) Schack, Email: moc.
Baohong Zhang, Email: moc. National Center for Biotechnology InformationU. BMC Genomics. Published online Sep 3. Author information Article notes Copyright and License information Disclaimer. Corresponding author. Received Apr 28; Accepted Aug This article has been cited by other articles in PMC. Abstract Background While RNA-sequencing RNA-seq is becoming a powerful technology in transcriptome profiling, one significant shortcoming of the first-generation RNA-seq protocol is that it does not retain the strand specificity of origin for each transcript.
Results Our results demonstrated a substantial impact of stranded RNA-seq on transcriptome profiling and gene expression measurements. Electronic supplementary material The online version of this article The Strand Of The Plot - Rename - Touch (Mixes) (CDr) Background RNA-sequencing RNA-seq is a next-generation sequencing technique that allows an in-depth look into the transcriptome [ 1 — 3 ].
Open in a separate window. Results and discussion The sample preparation, sequencing, and data analysis are detailed in the Methods section. Read mapping and counting Each replicate sample was sequenced by both non-stranded and stranded RNA-seq. Theoretical estimate of frequency and magnitude of gene overlap Every gene in Gencode Release 19 has genomic coordinates, and the frequency of overlapping genes can thus be calculated Fig.
Differential analysis The scatter plots of the gene expression profiles for the four replicate samples are shown in Fig. Table 1 The association between differential expression and gene overlapping is gene-type dependent. Exemplary differential expression genes For a given gene, if stranded and non-stranded RNA-seq report different expression levels, The Strand Of The Plot - Rename - Touch (Mixes) (CDr), which one is more reliable? Conclusions In this paper, we performed a side-by-side comparison of stranded and non-stranded RNA-seq, and investigated the gene overlap both in our practical whole blood RNA-seq dataset and from the theoretical perspective.
It's actually not right because there is no AUG, but hey, what the hell that's what you get from that coding strand. If your teacher told you to do this, be sure to ask him where the hell is the AUG!! Evil question. If you give the correct answer, it will almost certainly be marked wrong. Have your teacher fix this by specifying the 5' end of the given DNA strand, and whether this is the sense strand or antisense strand.
I am fairly sure your teacher meant to put a 3' in front of his strand and mention that it's the antisense strand. Trending News. Fire officials reveal where celebrity chef's blaze began. Kamala Harris's father is a prominent economist. These are the hottest ZIP codes across America.
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View credits, reviews, tracks and shop for the Vinyl release of Do The Strand on Discogs. Label: Island Records - 12 AT • Format: Vinyl 7 Roxy Music - Do The Strand (, Vinyl) | Discogs/5(8). When referring to DNA transcription, the coding strand is the DNA strand whose base sequence corresponds to the base sequence of the RNA transcript produced (although with thymine replaced by uracil).It is this strand which contains codons, while the non-coding strand contains faharderimarneusobisecocontge.co transcription, RNA Pol II binds to the non-coding strand, reads the anti-codons, and transcribes. The Strands Series is a series of books and short stories written by Gael Baudino between 19and published between and The majority of the plot occurs in a fictional land named Adria, which is based on Medieval Western Europe, with parts of later books occurring in s Denver, faharderimarneusobisecocontge.co many of the locations in Adria are fictional, the geography, culture, and language.
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Oct 04, · Introduction In the following blog post I am going to give a brief overview of what transcription is, a common pitfall in understanding it from a bioinformatics perspective, and how to implement a simple algorithm to convert (transcribe) a template DNA strand into an RNA strand. Transcription DNA Transcription in nature is the process of converting. Mar 02, · Provided to YouTube by Universal Music Group Do The Strand (Remastered) · Roxy Music The Best Of Roxy Music ℗ Virgin Records Ltd. Released on:
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A template strand is the term that refers to the strand used by DNA polymerase or RNA polymerase to attach complementary bases during DNA replication or RNA transcription, respectively; either molecule moves down the strand in the 3' to 5' direction, and at each subsequent base, it adds the complement of the current DNA base to the growing nucleic acid strand (which is thus created in the 5.
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