These values are generated through this pipeline by first aligning reads to the GRCh38 reference genome and then by quantifying the mapped reads. To facilitate harmonization across samples, all RNA-Seq reads are treated as unstranded during analyses.
STAR aligns each read group separately and then merges the resulting alignments into one. Following the methods used by the International Cancer Genome Consortium ICGC githubthe two-pass method includes a splice junction detection step, which is used to generate the final alignment.
This workflow outputs a genomic BAM file, which contains both aligned and unaligned reads. Files that were processed after Data Release 14 have associated transcriptomic and chimeric alignments in addition to the genomic alignment detailed above. This only applies to aliquots with at least one set of paired-end reads.
The chimeric BAM file contains reads that were mapped to different chromosomes or strands fusion alignments. The genomic alignment files contain chimeric and unaligned reads to facilitate the retrieval of all original reads. The transcriptomic alignment reports aligned reads with transcript coordinates rather than genomic coordinates. The transcriptomic alignment is also sorted differently to facilitate downstream analyses.
BAM index file pairing is not supported by this method of sorting, which does not allow for BAM slicing on these alignments. The splice-junction file for these alignments are also available. Note that version numbers may vary in files downloaded from the GDC Portal due to ongoing pipeline development and improvement.
The reads mapped to each gene are enumerated using HT-Seq-Count. Expression values are provided in a tab-delimited format. Files that were processed after Data Release 14 have an additional set of read counts that were produced by STAR during the alignment step. Below are two files that list genes that are completely encompassed by other genes and will likely display a value of zero. Normalized values should be used only within the context of the entire gene set.
Users are encouraged to normalize raw read count values if a subset of genes is investigated. The Fragments per Kilobase of transcript per Million mapped reads FPKM calculation normalizes read count by dividing it by the gene length and the total number of reads mapped to protein-coding genes.
Note: The read count is multiplied by a scalar 10 9 during normalization to account for the kilobase and 'million mapped reads' units. To facilitate the use of harmonized data in user-created pipelines, RNA-Seq gene expression is accessible in the GDC Data Portal at several intermediate steps in the pipeline. Aligned Reads. Gene Expression. Reads that were not aligned are included to facilitate the availability of raw read sets. A normalized expression value that takes into account each gene length and the number of reads mapped to all protein-coding genes.Metrics details.
Sweet osmanthus Osmanthus fragrans Lour. The flowering time of once-flowering cultivars in O. A hypothesis had been raised that genes related with flower opening might be up-regulated in response to relatively low temperature in O. Thus, our work was aimed to explore the underlying molecular mechanism of flower opening regulated by relatively low temperature in O. The cell size of adaxial and abaxial petal epidermal cells and ultrastructural morphology of petal cells at different developmental stages were observed.
The cell size of adaxial and abaxial petal epidermal cells increased gradually with the process of flower opening. The DEGs involved in cell wall metabolism, phytohormone signal transduction pathways, and eight kinds of transcription factors were analyzed in depth. Several unigenes involved in cell wall metabolism, phytohormone signal transduction pathway, and transcription factors with highly variable expression levels between different temperature treatments may be involved in petal cell expansion during flower opening process in response to the relatively low temperature.
These results could improve our understanding of the molecular mechanism of relatively-low-temperature-regulated flower opening of O. Osmanthus fragrans Lour. It is a small evergreen tree, grown as ornamental plants for its fragrant edible flowers.
Cultivars of O. The once-flowering cultivars bloom in autumn and the flowering time varies greatly in different areas, such as in Hangzhou, Shanghai, Nanjing, and Suzhou, or even in different years in the same area [ 3 ].
The researches on the flowering time of different cultivars indicated that relatively low temperature before blooming is the most important environmental factor determining the flower opening of O. However, the knowledge of molecular mechanism of flower opening in O.
In many higher plants, the growth of flower petals is the most remarkable process during flower opening. Flower petals are the most important component of reproductive organs and play vital roles in attracting the suitable pollinator s. Flower color, size, shape and appearance, which are determined by flower petal, are important traits appreciated by the breeders and consumers [ 5 ].StatQuest: Linear Discriminant Analysis (LDA) clearly explained.
One of the traits, the size of flower petal is determined by cell division at early phases of petal growth and cell expansion at later stages of flower opening [ 6 ]. Cell expansion is accompanied by a series of process including cell wall loosening, cellulose biosynthesis, polysaccharides conversion into soluble carbohydrate, ion and water uptake, and cytoskeleton rearrangement [ 7 ].
In wintersweet Chimonanthus praecoxthe expressions of EXP genes increase during flower opening [ 10 ]. The developing petals of carnation show high activities of cellulase and pectin esterase [ 11 ]. These findings reveal that petal growth relevant to flower opening is probably attributed to cell expansion.
Moreover, soluble carbohydrates depending on the degradation of polysaccharides can act as osmotically active compounds which could lower the osmotic water potential and facilitate water influx in order to allow cell expansion [ 12 ]. The concentration of soluble carbohydrates in the petals will increase in the flower opening process of plants such as carnation [ 13 ], rose [ 14 ], chrysanthemum [ 15 ], Tweedia caerulea [ 16 ], and lisianthus [ 17 ].
Cell expansion is regulated by both external factors, such as temperature, humidity, and the quality and quantity of light, and internal factors, such as the circadian clock and phytohormones [ 1819 ].
Phytohormones are the most important mediators regulating flower opening and could be affected by circadian factors or environmental factors. In this research, potted plants of O. This study would lay foundation on fully revealing the molecular mechanism of relatively-low-temperature-regulated flower opening of O. Developmental stages of sweet osmanthus flowers for SEM and TEM analysis were described as follows: stage 1 S1the outer bud scales unfurled and the inner bud scales still furled; S2, the bud became globular-shaped and the inside bracts covering the inflorescence was visible; S3, the inflorescence burst through bracts and the florets closely crowded; S4, initial flowering stage; S5, full flowering stage; S6, pollen-scattered stage.
These results were coincident with results in Gaillardia grandiflora [ 23 ], carnation [ 24 ] and T. However, in rose [ 25 ] and E. The same situation occurred in E. These results indicated that the petal cell expansion was accompanied by the enlargement of vacuole.Still getting the hang of the whole RNA-seq and gene annotation process.
One factor I have been thinking about lately and after reading a publication I would like to ask a question in regards to:. I generally assumed a value 'greater than' 0 would mean the transcript is expressed but I have read different. Can anyone shed light on this?
Can anyone explain? I also have 3 different conditions: control, low and medium. Ultimately what would be the best way to go about comparisons, does my method make sense? So far I have created some Venn diagrams to show transcripts present in each condition, shared between conditions, present in all conditions, heat maps using EdgeR and I am currently making use of Blast2GO for GO and annotation comparisons.
It is not clear to me if there can be any sensible way of determining such cutoff other than arbitrarily from a single sample alone. I took for granted that by loading the. Is this wrong, and calculating fold change then log2 in Excel wrong too? Any help greatly appreciated.
My idea is to see the change in gene representation over the different conditions as GO terms. I can't recommend using edgeR with normalized estimated counts. Perhaps you get vaguely correct results, perhaps not, it's tough to know since the counts kind of violate the statistical model used by edgeR.
If you're interested in looking at GO enrichment, just use the gene-level metrics. While one could theoretically hope to find different GO annotation per-transcript, this never occurs practically, at least.
Log In. Welcome to Biostar! Please log in to add an answer. FPKM matr I am comparing two treatment conditions Hello, I have 4 induced and 4 control samples technical replicates. I ran cuffdiff to find dif HI people, I am a bit new to all of the Bioinformatics but so far I have produced data from the Hi everyone, It'll be a bit long post, so please bear with me.
Hello, I am doing RNAseq analysis for the first time. I have two samples, control and treatment o Each treatment Hi dear all, I am trying to compare my RNA-seq data sets. This question is related to a question I asked here earlier. Since then, I have gotten the raw da Hello everyone, I need to represent the overlap of differentially expressed genes across multip Sorry for the basic question, but I've really confused myself.These three metrics attempt to normalize for sequencing depth and gene length.
With paired-end RNA-seq, two reads can correspond to a single fragment, or, if one read in the pair did not map, one read can correspond to a single fragment. The only difference is the order of operations. However, the effects of this difference are quite profound.
This makes it easier to compare the proportion of reads that mapped to a gene in each sample. In contrast, with RPKM and FPKM, the sum of the normalized reads in each sample may be different, and this makes it harder to compare samples directly. This is because the sum of the TPMs in both samples always add up to the same number so the denominator required to calculate the proportions is the same, regardless of what sample you are looking at.
This is because the denominator required to calculate the proportion could be different for the two samples. I was just pointed here by a colleague to help me understand the benefit of TPM over RPKM this is not my field and i think this correction is mistaken.
This measures the transcription rate for gene A. Please comment. This is basically a sum of sequencing depths of all genes, which is fundamentally different from the total number of mapped reads. Hi, I currently work with qPCR, but just recently was introduced to RNA-Seq ways to report results when a paper about the whole transcriptome of the organism I work with came out.
Could you give me a hand, please? This is similar to why the order of operations like multiplication, addition, brackets matters. Look at the toy examples. If RPKM is obtained first by normalizing the sequencing depth and then the gene length in kb. Because RPMK seems to be more correctly reflect the order of operation. Is there any biological explanation? If you do not consider the gene length, then you will consider a gene with more reads mapped to be expressed higher, while that may not be the case.Most of the times it's difficult to understand basic underlying methodology to calculate these units from mapped sequence data.
I have seen a lot of post of such normalization questions and their confusion among readers. Hence, I attempted here to explain these units in the much simpler way avoided complex mathematical expressions. The expression units provide a digital measure of the abundance of transcripts.
Normalized expression units are necessary to remove technical biases in sequenced data such as depth of sequencing more sequencing depth produces more read count for gene expressed at same level and gene length differences in gene length generate unequal reads count for genes expressed at the same level; longer the gene more the read count.
For example, You have sequenced one library with 5 million M reads. Among them, total 4 M matched to the genome sequence and reads matched to a given gene. When we map paired-end data, both reads or only one read with high quality from a fragment can map to reference sequence.
mRNA Analysis Pipeline
To avoid confusion or multiple counting, the fragments to which both or single read mapped is counted and represented for FPKM calculation. For example, You have sequenced one library with 5 M reads. Among them, total 4 M matched to the genome sequence and reads matched to a given gene with a length of bp. Order of operation is not the key point.
I read the paper carefully and definitely sure that the current post is wrong. Theory in biosciences. They are not ideal where cross-sample differential expression analysis is your aim; indeed, they render samples incomparable via differential expression analysis:. Please read this: A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis.
The Total Count and RPKM [FPKM] normalization methods, both of which are still widely in use, are ineffective and should be definitively abandoned in the context of differential analysis.
A review of RNA-Seq expression units. The first thing one should remember is that without between sample normalization a topic for a later postNONE of these units are comparable across experiments. This is a result of RNA-Seq being a relative measurement, not an absolute one. Can you please explain, where I am wrong in TPM calculation? If you read the paper, it clearly says, T is the total number of transcripts sampled by total sequenced reads. Please, let me know where I am wrong.
It should be stated up front that neither of these methods is optimal for conducting differential expression analysis across samples.
TPM for all transcripts in a sample shall add up to 1 million. Log In.Metrics details. In quantitative real-time polymerase chain reaction qRT-PCR experiments, accurate and reliable target gene expression results are dependent on optimal amplification of house-keeping genes HKGs. Goat Capra hircus is an economically important livestock species and plays an indispensable role in the world animal fiber and meat industry.
Unfortunately, uniform and reliable HKGs for skin research have not been identified in goat.
Therefore, this study seeks to identify a set of stable HKGs for the skin tissue of C. Four different experimental variables: 1 different development stages, 2 hair follicle cycle stages, 3 breeds, and 4 sampling sites were used for determination and validation. Moreover, a new algorithm for comprehensive analysis, ComprFinder, was developed and released. This study presents the first list of candidate HKGs for C. In addition, we also encourage researchers who perform candidate HKG evaluations and who require comprehensive analysis to adopt our new algorithm, ComprFinder.
In molecular biology research, determining the relative changes in target gene expression at the transcriptional level requires precise quantitative analysis. Furthermore, qRT-PCR is a commonly used technique due to its accuracy, sensitivity, reproducibility, and cost-effectiveness in analyzing gene expression [ 12 ].
The copy number of nucleic acid was calculated through the changes in real-time fluorescence reaction. The changes is typically reported as a cycle threshold value Ct in the comparative Ct method [ 3 ]. Ideal endogenous HKGs should exhibit consistent expression levels across all experimental conditions e. Unfortunately, no HKGs are stable across all experimental conditions, which means that each experimental system may need to use unique HKG s to accurately explore the specific research question being investigated.
Goat Capra hircus is an economically important livestock species as a source of meat, hair, and dairy products [ 11 ]. Skin tissue, as the largest biological organ with important functions including physical protection from injury and infection, thermal insulation, and providing the substrate for growing hair.
To reveal the molecular regulatory mechanism of hair follicle activity, it is necessary to clarify the pattern of target gene expression under different conditions, such as different stages of the hair follicle cycle. InBai et al. However, due to the limited number of animals used and testing only of commonly used HKGs, the previously published study [ 17 ] resulted in a limited impact.
The development of high-throughput RNA-seq technology provides a method of determining spatiotemporal expression at the transcriptome level, and provides a novel approach for the identification of HKGs [ 1819 ]. This strategy was successfully used to identify candidate HKGs for Artemisia sphaerocephala [ 7 ], Pyropia yezoensis [ 20 ], Euscaphis [ 21 ], Arabidopsis pumila [ 22 ], fish [ 23 ], tomato leaves [ 24 ], and holstein cows [ 25 ]. Therefore, we hypothesized that the novel, credible HKGs which serve goat skin research can be predicted and validated via transcriptome sequencing data.
In this study, the transcriptome dataset of 39 goat skin tissue samples was analyzed. Finally, the reliability of the recommended optimal HKGs was validated and confirmed. From a complete transcriptome dataset, the fragments per kilobase of exon model per million mapped reads FPKM of all transcripts from each sample were obtained.
This resulted in 15, unigenes being found for further selection. As shown in Fig. Potential HKGs were relatively highly expressed genes [ 8 ]. Most stable genes exhibited lower DPM values. Following this, genes This parameter reflects the range of extremum value, and the lowest MFC values are preferable. A Venn diagram was constructed for the 4-color blocks green, red, yellow, and blue corresponding to those used in Fig. This showed that genes Fig. In total, 12 candidate HKGs were analyzed in subsequent steps.The next step in the RNA-seq workflow is the differential expression analysis.
The goal of differential expression testing is to determine which genes are expressed at different levels between conditions. These genes can offer biological insight into the processes affected by the condition s of interest. The steps outlined in the gray box below we have already discussed, and we will now continue to describe the steps in an end-to-end gene-level RNA-seq differential expression workflow. So what does the count data actually represent?
The count data used for differential expression analysis represents the number of sequence reads that originated from a particular gene. The higher the number of counts, the more reads associated with that gene, and the assumption that there was a higher level of expression of that gene in the sample.
The differential expression analysis steps are shown in the flowchart below in green. First, the count data needs to be normalized to account for differences in library sizes and RNA composition between samples. Then, we will use the normalized counts to make some plots for QC at the gene and sample level. Finally, the differential expression analysis is performed using your tool of interest.
The first step in the DE analysis workflow is count normalization, which is necessary to make accurate comparisons of gene expression between samples. In this way the expression levels are more comparable between and within samples. Sequencing depth: Accounting for sequencing depth is necessary for comparison of gene expression between samples. In the example below, each gene appears to have doubled in expression in Sample A relative to Sample Bhowever this is a consequence of Sample A having double the sequencing depth.
NOTE: In the figure above, each pink and green rectangle represents a read aligned to a gene. Reads connected by dashed lines connect a read spanning an intron. Gene length: Accounting for gene length is necessary for comparing expression between different genes within the same sample. In the example, Gene X and Gene Y have similar levels of expression, but the number of reads mapped to Gene X would be many more than the number mapped to Gene Y because Gene X is longer. RNA composition: A few highly differentially expressed genes between samples, differences in the number of genes expressed between samples, or presence of contamination can skew some types of normalization methods.
Accounting for RNA composition is recommended for accurate comparison of expression between samples, and is particularly important when performing differential expression analyses [ 1 ].
In the example, if we were to divide each sample by the total number of counts to normalize, the counts would be greatly skewed by the DE gene, which takes up most of the counts for Sample Abut not Sample B. Most other genes for Sample A would be divided by the larger number of total counts and appear to be less expressed than those same genes in Sample B.
While normalization is essential for differential expression analyses, it is also necessary for exploratory data analysis, visualization of data, and whenever you are exploring or comparing counts between or within samples.
Therefore, you cannot compare the normalized counts for each gene equally between samples.
Therefore, we cannot directly compare the counts for XCR1 or any other gene between sampleA and sampleB because the total number of normalized counts are different between samples. A useful initial step in an RNA-seq analysis is often to assess overall similarity between samples:. Log2-transformed normalized counts are used to assess similarity between samples using Principal Component Analysis PCA and hierarchical clustering. Sample-level QC allows us to see how well our replicates cluster together, as well as, observe whether our experimental condition represents the major source of variation in the data.
Performing sample-level QC can also identify any sample outliers, which may need to be explored to determine whether they need to be removed prior to DE analysis. Principal Component Analysis PCA is a dimensionality reduction technique that finds the greatest amounts of variation in a dataset and assigns it to principal components. The principal component PC explaining the greatest amount of variation in the dataset is PC1, while the PC explaining the second greatest amount is PC2, and so on and so forth.
For a more detailed explanation, please see additional materials here. Generally, we focus on PC1 and PC2 which explain the largest amounts of variation in the data and plot them against each other.
In an ideal experiment, we would expect all replicates for each sample group to cluster together and the sample groups to cluster apart in the PCA plot as shown below.
In this example, the metadata for the experiment is displayed below. The main condition of interest is treatment.