RNAseq report for sample_report
This report includes summaries of data quality, data processing, and snapshots of results for your RNA-Seq study. This report should assist you to get a general picture of the study, to spot any irregularities in the sample or data, and to explore the most significant results in differential gene expression. Please consult our RNAseq report documentation on how to use this report.
General Statistics
Showing 8/16 rows and 9/18 columns.| Sample Name | M Seqs | % GC | % Reads PF | % Aligned | % Dups | % rRNA | % Assigned | M Assigned | Numbers of genes detected |
|---|---|---|---|---|---|---|---|---|---|
| Ctl_PE_1 | 7.9 | 48% | 99.4% | 94.0% | 5.2% | 0.15% | 77.7% | 6.0 | 25329 |
| Ctl_PE_2 | 7.4 | 49% | 99.1% | 93.2% | 4.7% | 0.22% | 78.2% | 5.5 | 25084 |
| Ctl_SE_1 | 73.0 | 46% | 99.8% | 91.2% | 53.0% | 0.15% | 72.7% | 51.5 | 36044 |
| Ctl_SE_2 | 94.7 | 47% | 99.6% | 91.1% | 56.5% | 0.21% | 73.3% | 67.4 | 37390 |
| Zika_PE_1 | 7.4 | 48% | 99.2% | 93.8% | 4.1% | 0.41% | 76.2% | 5.4 | 24902 |
| Zika_PE_2 | 7.6 | 48% | 99.1% | 93.5% | 4.1% | 0.39% | 76.7% | 5.6 | 24837 |
| Zika_SE_1 | 66.5 | 46% | 99.5% | 91.0% | 49.8% | 0.36% | 71.8% | 46.0 | 35522 |
| Zika_SE_2 | 76.3 | 47% | 99.6% | 91.0% | 52.1% | 0.38% | 71.4% | 52.5 | 36457 |
FastQC
FastQC is a quality control tool for high throughput sequence data, written by Simon Andrews at the Babraham Institute in Cambridge.
Only QC results of read 1 are plotted here. Please contact us for reads 2 QC plots if interested.
Sequence Quality Histograms
The mean quality value across each base position in the read.
To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).
Taken from the FastQC help:
The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.
Per Sequence Quality Scores
The number of reads with average quality scores. Shows if a subset of reads has poor quality.
From the FastQC help:
The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.
Per Base Sequence Content
The proportion of each base position for which each of the four normal DNA bases has been called.
To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.
To see the data as a line plot, as in the original FastQC graph, click on a sample track.
From the FastQC help:
Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.
In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.
It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.
Rollover for sample name
Per Sequence GC Content
The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.
From the FastQC help:
This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.
In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.
An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.
Per Base N Content
The percentage of base calls at each position for which an N was called.
From the FastQC help:
If a sequencer is unable to make a base call with sufficient confidence then it will
normally substitute an N rather than a conventional base call. This graph shows the
percentage of base calls at each position for which an N was called.
It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.
Sequence Length Distribution
The distribution of fragment sizes (read lengths) found. See the FastQC help
Overrepresented sequences
The total amount of overrepresented sequences found in each library.
FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as over represented.
Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.
From the FastQC Help:
A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.
FastQC lists all of the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.
Adapter Content
The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.
Note that only samples with ≥ 0.1% adapter contamination are shown.
There may be several lines per sample, as one is shown for each adapter detected in the file.
From the FastQC Help:
The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.
Trim Galore
Trim Galore is a wrapper around Cutadapt and FastQC to consistently apply adpater and quality trimming to FastQ files.
Filtered Reads
This plot shows the number of reads (SE) / pairs (PE) removed by Trim Galore.
Trimmed Sequence Lengths
This plot shows the number of reads with certain lengths of adapter trimmed. Quality trimmed and hard trimmed sequences are not included.
Obs/Exp shows the raw counts divided by the number expected due to sequencing errors. A defined peak may be related to adapter length. See the cutadapt documentation for more information on how these numbers are generated.
STAR
STAR is an ultrafast universal RNA-seq aligner.
The sorted BAM files produced by STAR can be downloaded in the Download data section.
Alignment Scores
RSeQC
RSeQC package provides a number of useful modules that can comprehensively evaluate high throughput RNA-seq data.
Read Distribution
Read Distribution calculates how mapped reads are distributed over genome features.
Inner Distance
Inner Distance calculates the inner distance (or insert size) between two paired RNA reads. Note that this can be negative if fragments overlap.
Read Duplication
read_duplication.py calculates how many alignment positions have a certain number of exact duplicates. Note - plot truncated at 500 occurrences and binned.
Junction Annotation
Junction annotation compares detected splice junctions to a reference gene model. An RNA read can be spliced 2 or more times, each time is called a splicing event.
Junction Saturation
Junction Saturation counts the number of known splicing junctions that are observed in each dataset. If sequencing depth is sufficient, all (annotated) splice junctions should be rediscovered, resulting in a curve that reaches a plateau. Missing low abundance splice junctions can affect downstream analysis.
Infer experiment
Infer experiment counts the percentage of reads and read pairs that match the strandedness of overlapping transcripts. It can be used to infer whether RNA-seq library preps are stranded (sense or antisense).
Bam Stat
All numbers reported in millions.
QualiMap
QualiMap is a platform-independent application to facilitate the quality control of alignment sequencing data and its derivatives like feature counts.
Gene Coverage Profile
Mean distribution of coverage depth across the length of all mapped transcripts.
There are currently three main approaches to map reads to transcripts in an RNA-seq experiment: mapping reads to a reference genome to identify expressed transcripts that are annotated (and discover those that are unknown), mapping reads to a reference transcriptome, and de novo assembly of transcript sequences (Conesa et al. 2016).
For RNA-seq QC analysis, QualiMap can be used to assess alignments produced by the first of these approaches. For input, it requires a GTF annotation file along with a reference genome, which can be used to reconstruct the exon structure of known transcripts. QualiMap uses this information to calculate the depth of coverage along the length of each annotated transcript. For a set of reads mapped to a transcript, the depth of coverage at a given base position is the number of high-quality reads that map to the transcript at that position (Sims et al. 2014).
QualiMap calculates coverage depth at every base position of each annotated transcript. To enable meaningful comparison between transcripts, base positions are rescaled to relative positions expressed as percentage distance along each transcript (0%, 1%, …, 99%). For the set of transcripts with at least one mapped read, QualiMap plots the cumulative mapped-read depth (y-axis) at each relative transcript position (x-axis). This plot shows the gene coverage profile across all mapped transcripts for each read dataset. It provides a visual way to assess positional biases, such as an accumulation of mapped reads at the 3′ end of transcripts, which may indicate poor RNA quality in the original sample (Conesa et al. 2016).
Picard
Picard is a set of Java command line tools for manipulating high-throughput sequencing data.
Mark Duplicates
Number of reads, categorised by duplication state. Pair counts are doubled - see help text for details.
The table in the Picard metrics file contains some columns referring read pairs and some referring to single reads.
To make the numbers in this plot sum correctly, values referring to pairs are doubled according to the scheme below:
READS_IN_DUPLICATE_PAIRS = 2 * READ_PAIR_DUPLICATESREADS_IN_UNIQUE_PAIRS = 2 * (READ_PAIRS_EXAMINED - READ_PAIR_DUPLICATES)READS_IN_UNIQUE_UNPAIRED = UNPAIRED_READS_EXAMINED - UNPAIRED_READ_DUPLICATESREADS_IN_DUPLICATE_PAIRS_OPTICAL = 2 * READ_PAIR_OPTICAL_DUPLICATESREADS_IN_DUPLICATE_PAIRS_NONOPTICAL = READS_IN_DUPLICATE_PAIRS - READS_IN_DUPLICATE_PAIRS_OPTICALREADS_IN_DUPLICATE_UNPAIRED = UNPAIRED_READ_DUPLICATESREADS_UNMAPPED = UNMAPPED_READS
Preseq
Preseq estimates the complexity of a library, showing how many additional unique reads are sequenced for increasing total read count. A shallow curve indicates complexity saturation. The dashed line shows a perfectly complex library where total reads = unique reads.
Complexity curve
Note that the x axis is trimmed at the point where all the datasets show 80% of their maximum y-value, to avoid ridiculous scales.
DupRadar
provides duplication rate quality control for RNA-Seq datasets. Highly expressed genes can be expected to have a lot of duplicate reads, but high numbers of duplicates at low read counts can indicate low library complexity with technical duplication. This plot shows the general linear models - a summary of the gene duplication distributions.
Biotype Counts
shows reads overlapping genomic features of different biotypes, counted by featureCounts.
featureCounts
Subread featureCounts is a highly efficient general-purpose read summarization program that counts mapped reads for genomic features such as genes, exons, promoter, gene bodies, genomic bins and chromosomal locations.
Distances/similarities between samples
This section plots the distances or similarities between samples in the form of heatmap, PCA, and/or MDS plots.
Similarity matrix of samples
The similarities (Pearson correlation coefficient) between samples are visualized here in the form of heatmap. Larger values indicate higher similarity between samples. The similarities were calculated using normalized and 'rlog' transformed read counts of all genes using DESeq2.
Multidimensional scaling analysis of samples
Multidimensional scaling was conducted to visualize the distance/similarity between samples. Top 500 genes with highest variance among samples were used to make this plot.
Top gene expression patterns
Normalized read counts of top genes with highest variance, calculated using DESeq2. Values plotted in Log2 scale after centering per gene. A static version of this figure can be download in the Download data section.
Differential gene expression
DESeq2 calculates the expression levels of genes and conducts statistical analysis of differential gene expression.
Summary table of gene differential expression
General statistics of differentially expressed genes in pairwise comparisons. Genes with adjusted p-values smaller than 0.05 were considered differentially expressed.
| Comparison(cond.1_cond.2) | Higher in condition 1 | Higher in condition 2 | Not differentially expressed | Did not pass filter |
|---|---|---|---|---|
| Control_vs_Zika | 698 | 536 | 21333 | 21227 |
Scatter plot
Scatter plot is a simple and straightforward way to visualize differential gene expression results. Expression levels of genes in one condition are shown on X-axis while those in other are shown on Y-axis.
The scatter plots here include differentially expressed genes (up to the first 1000 genes) and up to 1000 randomly selected non-differentially expressed genes. You can download the scatter plots with all genes in the Download data section.
Red dots represent differentially expressed genes (adjusted p-values<0.05). Grey dots represent non-differentially expressed genes. Count data transformation was carried out using the 'rlog' method in DESeq2
MA plot
MA plot is a type of visualization of differential gene expression results often used in publications. Expression levels are shown on X-axis while log2 of fold changes are shown on Y-axis.
The MA plots here include differentially expressed genes (up to the first 1000 genes) and up to 1000 randomly selected non-differentially expressed genes. You can download the MA plots with all genes in the Download data section.
Red dots represent differentially expressed genes (adjusted p-values<0.05). Grey dots represent non-differentially expressed genes. Shrinkage of effect size was carried out using the 'normal' method in DESeq2
Top differentially expressed genes in comparison Control vs. Zika
Top 50 differentially expressed genes, ranked by FDR, in comparison Control vs. Zika. Full DESeq2 results can be downloaded in the Download data section.
Genes with positive Log2 fold changes have higher expression in Control. Genes with negative Log2 fold changes have higher expression in Zika.
| Rank | Gene name | Mean counts | Log2 Fold change | False discovery rate |
|---|---|---|---|---|
| 1 | HERPUD1 | 2354 | -2.42 | 6.3e-130 |
| 2 | SLC7A5 | 5516 | -2.49 | 1.4e-118 |
| 3 | VEGFA | 2090 | -2.11 | 9.6e-116 |
| 4 | XBP1 | 2510 | -2.50 | 5.6e-111 |
| 5 | CEBPB | 380 | -3.06 | 1.8e-101 |
| 6 | SESN2 | 1805 | -2.11 | 1.6e-98 |
| 7 | ASNS | 334 | -3.16 | 7.2e-93 |
| 8 | SAFB2 | 2684 | -1.83 | 1.1e-92 |
| 9 | MTHFD2 | 1841 | -2.22 | 9.9e-90 |
| 10 | CREB3L2 | 1531 | -1.92 | 1.8e-86 |
| 11 | SLC3A2 | 5135 | -1.72 | 2.5e-79 |
| 12 | DDIT3 | 639 | -2.89 | 3.0e-75 |
| 13 | CHAC1 | 522 | -2.96 | 1.5e-74 |
| 14 | WNT8B | 1034 | 2.09 | 1.0e-73 |
| 15 | STC2 | 322 | -2.81 | 1.2e-72 |
| 16 | RRM2 | 1101 | 2.39 | 2.0e-71 |
| 17 | SEC24D | 617 | -3.04 | 2.0e-68 |
| 18 | CARS | 2699 | -2.16 | 1.1e-67 |
| 19 | YARS | 3068 | -1.65 | 1.7e-64 |
| 20 | ULBP1 | 247 | -3.29 | 4.2e-60 |
| 21 | TPBG | 5903 | 2.09 | 5.0e-54 |
| 22 | TRIB3 | 656 | -2.26 | 2.8e-49 |
| 23 | KLF15 | 366 | -2.31 | 5.3e-47 |
| 24 | MCM4 | 2011 | 1.72 | 1.2e-44 |
| 25 | AKAP17A | 2079 | -1.48 | 7.0e-43 |
| 26 | CDC20B | 110 | 3.61 | 7.5e-43 |
| 27 | SHMT2 | 3485 | -1.73 | 1.4e-42 |
| 28 | MCM6 | 1154 | 1.87 | 1.5e-41 |
| 29 | TSPYL2 | 1286 | -1.88 | 3.3e-41 |
| 30 | MFGE8 | 1023 | 1.94 | 2.5e-40 |
| 31 | IPO5P1 | 605 | 1.81 | 1.7e-39 |
| 32 | TTF2 | 337 | 2.31 | 1.7e-39 |
| 33 | PHGDH | 4793 | -1.33 | 2.9e-39 |
| 34 | MCM3 | 1466 | 1.70 | 1.1e-38 |
| 35 | E2F1 | 755 | 1.70 | 6.3e-38 |
| 36 | C21orf58 | 434 | 2.32 | 3.2e-37 |
| 37 | PCK2 | 284 | -2.32 | 1.0e-36 |
| 38 | SLC1A4 | 1523 | -1.38 | 1.6e-36 |
| 39 | EIF4EBP1 | 566 | -1.94 | 1.0e-34 |
| 40 | AC005908.3 | 431 | 2.01 | 1.3e-33 |
| 41 | SLC7A11 | 1479 | -2.57 | 9.4e-33 |
| 42 | ANKRD33B | 414 | -1.88 | 1.1e-32 |
| 43 | CCNB1 | 1168 | 1.74 | 1.2e-32 |
| 44 | GDF10 | 1047 | 1.83 | 1.4e-31 |
| 45 | NCAPH | 433 | 1.78 | 1.5e-31 |
| 46 | FANCD2 | 750 | 1.59 | 8.0e-31 |
| 47 | MAPK15 | 542 | 2.39 | 1.4e-30 |
| 48 | BAMBI | 564 | -1.66 | 4.8e-30 |
| 49 | MCM2 | 1104 | 1.41 | 5.9e-30 |
| 50 | SFXN2 | 238 | 2.22 | 6.3e-30 |
Gene set enrichment analysis
g:Profiler performs functional enrichment analysis also known as gene set enrichment analysis on input gene list. It maps genes to known functional information sources and detects statistically significantly enriched terms.
Summary table of gene set enrichment analysis
General statistics of gene set enrichment analysis in pairwise comparisons. Gene sets with false discovery rate smaller than 0.05 were considered enriched.
| Comparison(cond.1_cond.2) | Higher in condition 1 | Higher in condition 2 | Not enriched |
|---|---|---|---|
| Control_vs_Zika | 207 | 308 | 12169 |
Top enriched gene sets in comparison Control vs. Zika
Top 30 gene sets, ranked by p-value, in comparison Control vs. Zika. Full g:Profiler results can be downloaded in the Download data section.
| Rank | Gene set name | Gene set category | Adjusted p-value | Expression pattern |
|---|---|---|---|---|
| 1 | DNA-templated DNA replication | GO:Biological Process | 0.000 | Higher in Control |
| 2 | DNA replication | GO:Biological Process | 0.000 | Higher in Control |
| 3 | cell cycle process | GO:Biological Process | 0.000 | Higher in Control |
| 4 | response to endoplasmic reticulum stress | GO:Biological Process | 0.000 | Higher in Zika |
| 5 | mitotic cell cycle process | GO:Biological Process | 0.000 | Higher in Control |
| 6 | cell cycle | GO:Biological Process | 0.000 | Higher in Control |
| 7 | mitotic cell cycle | GO:Biological Process | 0.000 | Higher in Control |
| 8 | DNA strand elongation | Reactome Pathway | 0.000 | Higher in Control |
| 9 | double-strand break repair via break-induced replication | GO:Biological Process | 0.000 | Higher in Control |
| 10 | DNA metabolic process | GO:Biological Process | 0.000 | Higher in Control |
| 11 | DNA unwinding involved in DNA replication | GO:Biological Process | 0.000 | Higher in Control |
| 12 | chromosome organization | GO:Biological Process | 0.000 | Higher in Control |
| 13 | Unwinding of DNA | Reactome Pathway | 0.000 | Higher in Control |
| 14 | endoplasmic reticulum unfolded protein response | GO:Biological Process | 0.000 | Higher in Zika |
| 15 | DNA duplex unwinding | GO:Biological Process | 0.000 | Higher in Control |
| 16 | nuclear division | GO:Biological Process | 0.000 | Higher in Control |
| 17 | cell cycle phase transition | GO:Biological Process | 0.000 | Higher in Control |
| 18 | intrinsic apoptotic signaling pathway in response to endoplasmic reticulum stress | GO:Biological Process | 0.000 | Higher in Zika |
| 19 | Cell Cycle Mitotic | Reactome Pathway | 0.000 | Higher in Control |
| 20 | cell cycle checkpoint signaling | GO:Biological Process | 0.000 | Higher in Control |
| 21 | DNA geometric change | GO:Biological Process | 0.000 | Higher in Control |
| 22 | DNA repair | GO:Biological Process | 0.000 | Higher in Control |
| 23 | DNA replication initiation | GO:Biological Process | 0.000 | Higher in Control |
| 24 | response to topologically incorrect protein | GO:Biological Process | 0.000 | Higher in Zika |
| 25 | cellular response to topologically incorrect protein | GO:Biological Process | 0.000 | Higher in Zika |
| 26 | regulation of cell cycle process | GO:Biological Process | 0.000 | Higher in Control |
| 27 | response to unfolded protein | GO:Biological Process | 0.000 | Higher in Zika |
| 28 | cellular response to unfolded protein | GO:Biological Process | 0.000 | Higher in Zika |
| 29 | regulation of response to endoplasmic reticulum stress | GO:Biological Process | 0.000 | Higher in Zika |
| 30 | Cell Cycle | Reactome Pathway | 0.000 | Higher in Control |
Download data
This section contains links to download your original data, and data and/or images generated by various bioinformatics tools. There may be files for each sample, files for all samples, and files for group comparisons. To download individual files, click on the corresponding links. There are also instructions at the bottom of the this section if you want to download everything in batch.
Links in this section expire after 270 days. If you want to download files after that, please contact us.
Sample level files
Files concerning all samples
These files provide an overview of all samples (some of these are already displayed interactively for you in sections above):- Raw read counts of genes
- Normalized read counts of genes
- MDS plot of samples
- Similarity matrix of samples
- Heatmap of expression of top genes
Comparison level files
Instructions to download all files
- Download a script to download all files. We assume it is in your Downloads folder.
- Find and open Terminal(Mac/Linux) or Windows Powershell(Windows).
- Type
cd ~/Downloadsand Enter. (If your download folder is different, please change accordingly) - Copy and Paste
bash download_links.ps1(Mac/Linux) orPowershell.exe -ExecutionPolicy Bypass -File .\download_links.ps1(Windows) and Enter.
Software Versions
Software versions are collected at run time from the software output. This pipeline is adapted from nf-core RNAseq pipeline.
- RNAseq pipeline
- v2.1.0
- Nextflow
- v22.10.4
- FastQC
- v0.11.9
- Trim Galore!
- v0.6.6
- STAR
- v2.6.1d
- Samtools
- v1.9
- Preseq
- v2.0.3
- Picard MarkDuplicates
- v2.23.9
- dupRadar
- v1.18.0
- RSeQC
- v4.0.0
- Qualimap
- v2.2.2-dev
- featureCounts
- v2.0.1
- DESeq2
- v1.28.0
- gProfiler
- v1.0.0
Workflow Summary
This section summarizes important parameters used in the pipeline. They were collected when the pipeline was started.
- Genome
- GRCh38
- DESeq2 FDR cutoff
- 0.05
- DESeq2 Log2FC cutoff
- 0.585
- gProfiler FDR cutoff
- 0.05
- Trimming
- only using Illumina adatpers
- Strandedness
- None
- Library Prep
- Illumina RNA kits
Report generated on 2024-03-14, 23:35.
