metaseqR report

Summary

Analysis summary

Summary: The gene counts table was normalized for inherent systematic or experimental biases (e.g. sequencing depth, gene length, GC content bias etc.) using the Bioconductor package DESeq after removing genes that had zero counts over all the RNA-Seq samples (29765 genes). The output of the normalization algorithm was a table with normalized counts, which can be used for differential expression analysis with statistical algorithms developed specifically for count data. Prior to the statistical testing procedure, the gene read counts were filtered for possible artifacts that could affect the subsequent statistical testing procedures. Genes presenting any of the following were excluded from further analysis: i) genes with length less than 500 (2759 genes), ii) genes whose average reads per 100 bp was less than the 25^th quantile of the total normalized distribution of average reads per 100bp (0 genes with cutoff value 0.05083 average reads per 100 bp), iii) genes with read counts below the median read counts of the total normalized count distribution (8222 genes with cutoff value 9 normalized read counts). The total number of genes excluded due to the application of gene filters was 4461. The total (unified) number of genes excluded due to the application of all filters was 38925. The resulting gene counts table was subjected to differential expression analysis for the contrasts Tbil22 versus Tdil22 using the Bioconductor package DESeq. The final numbers of differentially expressed genes were (per contrast): for the contrast Tbil22 versus Tdil22, 905 (162) statistically significant genes were found with a p-value (FDR or adjusted p-value) threshold of 0.05 and of these, 540 (88) were up-regulated, 348 (74) were down-regulated and 17 (0) were not differentially expressed according to an absolute fold change cutoff value of 1 in log₂ scale. Literature references for all the algorithms used can be found at the end of this report.

Input options

Read counts file: imported custom data frame
Conditions: Tbil22, Tdil22
Samples included: SK3R4_Tbil22, SK3R5_Tbil22, SK3R6_Tbil22, SK3R10_Tdil22_9, SK3R11_Tdil22_10, SK3R12_Tdil22_11
Samples excluded: none
Requested contrasts: Tbil22_vs_Tdil22
Library sizes: not available
Annotation: embedded
Organism:
Annotation source: Ensembl genomes
Count type: utr
Exon filters: none applied
Gene filters: length, avg.reads, expression, biotype

length
- length: 500

avg.reads
- average.per.bp: 100
- quantile: 0.25

expression
- median: TRUE
- mean: FALSE
- quantile: NA
- known: NA
- custom: NA

biotype
- pseudogene: FALSE
- snRNA: FALSE
- protein_coding: FALSE
- antisense: FALSE
- miRNA: FALSE
- snoRNA: FALSE
- lincRNA: FALSE
- processed_transcript: FALSE
- misc_RNA: FALSE
- rRNA: TRUE
- sense_intronic: FALSE
- sense_overlapping: FALSE
- polymorphic_pseudogene: FALSE
- IG_C_gene: FALSE
- IG_J_gene: FALSE
- IG_D_gene: FALSE
- IG_LV_gene: FALSE
- IG_V_gene: FALSE
- IG_V_pseudogene: TRUE
- TR_V_gene: FALSE
- TR_V_pseudogene: TRUE
- three_prime_overlapping_ncrna: FALSE

Filter application: after normalization
Normalization algorithm: DESeq
Normalization arguments: locfunc

[[list(locfunc = function (x, na.rm = FALSE) standardGeneric("median"))locfunc

Statistical algorithm(s): DESeq
Statistical arguments for DESeq: method, sharingMode, fitType

method: blind
sharingMode: fit-only
fitType: local

Meta-analysis method: no meta-analysis, reported p-values from the first supplied statistical algorithm
Multiple testing correction: Benjamini-Hochberg FDR
p-value threshold: 0.05
Logarithmic tranformation offset: 1
Analysis preset: not available
Quality control plots: multidimensional scaling, biotype detection, biotype counts, sample and biotype saturation, filtered biotypes, correlation heatmap and correlogram, pairwise scatterplots between samples, boxplots, GC-content bias, transcript length bias, DEG biotype detection, volcano plot, DEG heatmap
Figure format: png, pdf
Output directory: /data/images/proton2/run374/SKobold/metaseqr_quantseq_Tbil22VSTdil22
Output data: Annotation, p-value, Adjusted p-value (FDR), Fold change, Statistics, Read counts
Output scale(s): log2 scale, Reads per Gene Model
Output values: Normalized values
Output statistics: Mean
Total run time: 03 minutes 11 seconds

Filtered genes

Number of filtered genes: 38925 which is the union of

Filtered because of zero reads: 29765
Filtered because of exon filters: 0
Filtered because of gene filters: 9160 which is the union of
- length: 2759 genes with filter cutoff value 500
- avg.reads: 1677 genes with filter cutoff value 0.05083238
- expression: 8222 genes further decomposed to (filter name, filtered genes, filter cutoff):
- - median: 8222 genes with filter cutoff value 9
- biotype: 24 genes with filter cutoff value rRNA, IG_V_pseudogene, TR_V_pseudogene

Differentially expressed genes

Number of differentially expressed genes per contrast:

Tbil22_vs_Tdil22: 905 (162) statistically significant genes of which 540 (88) up regulated, 348 (74) down regulated and 17 (0) not differentially expressed according to a p-value (FDR or adjusted p-value) threshold of 0.05 and an absolute fold change cutoff value of 1 in log₂ scale.

Run log

[ 2017-12-12 17:01:44 ] FATAL Condition names in sample list and contrast list do not match! Check if the contrasts follow the appropriate format (e.g. "_vs_" separating contrasting conditions...
[ 2017-12-12 17:01:44 ] WARN "yes" and "no" for read strandedness have been deprecated. Please use "forward", "forward" or "no". Replacing "yes" with "forward"...
[ 2017-12-12 17:01:44 ] INFO Converting annotation to GenomicRanges object...
[ 2017-12-12 17:01:44 ] INFO Reading bam file SK3R7-Tdctrl2.bam for sample with name SK3R7_Tdctrl2. This might take some time...
[ 2017-12-12 17:01:44 ] INFO Reading bam file SK3R8-Tdctrl3.bam for sample with name SK3R8_Tdctrl3. This might take some time...
[ 2017-12-12 17:01:44 ] INFO Reading bam file SK3R9-Tdctrl6.bam for sample with name SK3R9_Tdctrl6. This might take some time...
[ 2017-12-12 17:01:44 ] INFO Reading bam file SK3R10-Tdil22-9.bam for sample with name SK3R10_Tdil22_9. This might take some time...
[ 2017-12-12 17:01:45 ] INFO Reading bam file SK3R11-Tdil22-10.bam for sample with name SK3R11_Tdil22_10. This might take some time...
[ 2017-12-12 17:01:45 ] INFO Reading bam file SK3R12-Tdil22-11.bam for sample with name SK3R12_Tdil22_11. This might take some time...
[ 2017-12-12 17:01:48 ] INFO Counting reads overlapping with given annotation...
[ 2017-12-12 17:01:48 ] INFO ...for single-end reads...
[ 2017-12-12 17:01:48 ] INFO ...assuming forward sequenced reads...
[ 2017-12-12 17:01:48 ] INFO Counting reads overlapping with given annotation...
[ 2017-12-12 17:01:48 ] INFO ...for single-end reads...
[ 2017-12-12 17:01:48 ] INFO ...assuming forward sequenced reads...
[ 2017-12-12 17:01:49 ] INFO Counting reads overlapping with given annotation...
[ 2017-12-12 17:01:49 ] INFO ...for single-end reads...
[ 2017-12-12 17:01:49 ] INFO ...assuming forward sequenced reads...
[ 2017-12-12 17:01:50 ] INFO Counting reads overlapping with given annotation...
[ 2017-12-12 17:01:50 ] INFO ...for single-end reads...
[ 2017-12-12 17:01:50 ] INFO ...assuming forward sequenced reads...
[ 2017-12-12 17:01:50 ] INFO Counting reads overlapping with given annotation...
[ 2017-12-12 17:01:50 ] INFO ...for single-end reads...
[ 2017-12-12 17:01:50 ] INFO ...assuming forward sequenced reads...
[ 2017-12-12 17:01:51 ] INFO Counting reads overlapping with given annotation...
[ 2017-12-12 17:01:51 ] INFO ...for single-end reads...
[ 2017-12-12 17:01:51 ] INFO ...assuming forward sequenced reads...
[ 2017-12-12 17:15:49 ] INFO 2017-12-12 17:15:49: Data processing started...
[ 2017-12-12 17:15:49 ] INFO Read counts file: imported custom data frame
[ 2017-12-12 17:15:49 ] INFO Conditions: Tbil22, Tdil22
[ 2017-12-12 17:15:49 ] INFO Samples to include: SK3R4_Tbil22, SK3R5_Tbil22, SK3R6_Tbil22, SK3R10_Tdil22_9, SK3R11_Tdil22_10, SK3R12_Tdil22_11
[ 2017-12-12 17:15:49 ] INFO Samples to exclude: none
[ 2017-12-12 17:15:49 ] INFO Requested contrasts: Tbil22_vs_Tdil22
[ 2017-12-12 17:15:49 ] INFO Annotation: embedded
[ 2017-12-12 17:15:49 ] INFO Organism: custom
[ 2017-12-12 17:15:49 ] INFO Reference source: ensembl
[ 2017-12-12 17:15:49 ] INFO Count type: utr
[ 2017-12-12 17:15:49 ] INFO Exon filters: none applied
[ 2017-12-12 17:15:49 ] INFO Gene filters: length, avg.reads, expression, biotype
[ 2017-12-12 17:15:49 ] INFO length:
[ 2017-12-12 17:15:49 ] INFO length: 500
[ 2017-12-12 17:15:49 ] INFO avg.reads:
[ 2017-12-12 17:15:49 ] INFO average.per.bp: 100
[ 2017-12-12 17:15:49 ] INFO quantile: 0.25
[ 2017-12-12 17:15:49 ] INFO expression:
[ 2017-12-12 17:15:49 ] INFO median: TRUE
[ 2017-12-12 17:15:49 ] INFO mean: FALSE
[ 2017-12-12 17:15:49 ] INFO quantile: NA
[ 2017-12-12 17:15:49 ] INFO known: NA
[ 2017-12-12 17:15:49 ] INFO custom: NA
[ 2017-12-12 17:15:49 ] INFO biotype:
[ 2017-12-12 17:15:49 ] INFO pseudogene: FALSE
[ 2017-12-12 17:15:49 ] INFO snRNA: FALSE
[ 2017-12-12 17:15:49 ] INFO protein_coding: FALSE
[ 2017-12-12 17:15:49 ] INFO antisense: FALSE
[ 2017-12-12 17:15:49 ] INFO miRNA: FALSE
[ 2017-12-12 17:15:49 ] INFO snoRNA: FALSE
[ 2017-12-12 17:15:49 ] INFO lincRNA: FALSE
[ 2017-12-12 17:15:49 ] INFO processed_transcript: FALSE
[ 2017-12-12 17:15:49 ] INFO misc_RNA: FALSE
[ 2017-12-12 17:15:49 ] INFO rRNA: TRUE
[ 2017-12-12 17:15:49 ] INFO sense_intronic: FALSE
[ 2017-12-12 17:15:49 ] INFO sense_overlapping: FALSE
[ 2017-12-12 17:15:49 ] INFO polymorphic_pseudogene: FALSE
[ 2017-12-12 17:15:49 ] INFO IG_C_gene: FALSE
[ 2017-12-12 17:15:49 ] INFO IG_J_gene: FALSE
[ 2017-12-12 17:15:49 ] INFO IG_D_gene: FALSE
[ 2017-12-12 17:15:49 ] INFO IG_LV_gene: FALSE
[ 2017-12-12 17:15:49 ] INFO IG_V_gene: FALSE
[ 2017-12-12 17:15:49 ] INFO IG_V_pseudogene: TRUE
[ 2017-12-12 17:15:49 ] INFO TR_V_gene: FALSE
[ 2017-12-12 17:15:49 ] INFO TR_V_pseudogene: TRUE
[ 2017-12-12 17:15:49 ] INFO three_prime_overlapping_ncrna: FALSE
[ 2017-12-12 17:15:49 ] INFO Filter application: postnorm
[ 2017-12-12 17:15:49 ] INFO Normalization algorithm: deseq
[ 2017-12-12 17:15:49 ] INFO Normalization arguments:
[ 2017-12-12 17:15:49 ] INFO locfunc:
[ 2017-12-12 17:15:49 ] INFO [[
[ 2017-12-12 17:15:49 ] INFO list(locfunc = function (x, na.rm = FALSE) standardGeneric("median"))
[ 2017-12-12 17:15:49 ] INFO locfunc
[ 2017-12-12 17:15:49 ] INFO Statistical algorithm: deseq
[ 2017-12-12 17:15:49 ] INFO Statistical arguments:
[ 2017-12-12 17:15:49 ] INFO deseq: blind, fit-only, local
[ 2017-12-12 17:15:49 ] INFO Meta-analysis method: none
[ 2017-12-12 17:15:49 ] INFO Multiple testing correction: BH
[ 2017-12-12 17:15:49 ] INFO p-value threshold: 0.05
[ 2017-12-12 17:15:49 ] INFO Logarithmic transformation offset: 1
[ 2017-12-12 17:15:49 ] INFO Quality control plots: mds, biodetection, countsbio, saturation, readnoise, filtered, correl, pairwise, boxplot, gcbias, lengthbias, biodist, volcano, deheatmap
[ 2017-12-12 17:15:49 ] INFO Figure format: png, pdf
[ 2017-12-12 17:15:49 ] INFO Output directory: /data/images/proton2/run374/SKobold/metaseqr_quantseq_Tbil22VSTdil22
[ 2017-12-12 17:15:49 ] INFO Output data: annotation, p.value, adj.p.value, fold.change, stats, counts, flags
[ 2017-12-12 17:15:49 ] INFO Output scale(s): log2, rpgm
[ 2017-12-12 17:15:49 ] INFO Output values: normalized
[ 2017-12-12 17:15:49 ] INFO Output statistics: mean
[ 2017-12-12 17:15:49 ] INFO Reading custom external gene annotation for from /data/results/tools/rnaseq/metaseqr/mm10/gene_data_mm10.txt.gz...
[ 2017-12-12 17:15:50 ] INFO Checking chromosomes in transcript counts and gene annotation...
[ 2017-12-12 17:15:50 ] INFO Processing transcripts...
[ 2017-12-12 17:15:50 ] INFO Separating transcripts (UTR regions) per gene for SK3R4_Tbil22...
[ 2017-12-12 17:16:04 ] INFO Separating transcripts (UTR regions) per gene for SK3R5_Tbil22...
[ 2017-12-12 17:16:17 ] INFO Separating transcripts (UTR regions) per gene for SK3R6_Tbil22...
[ 2017-12-12 17:16:32 ] INFO Separating transcripts (UTR regions) per gene for SK3R10_Tdil22_9...
[ 2017-12-12 17:16:48 ] INFO Separating transcripts (UTR regions) per gene for SK3R11_Tdil22_10...
[ 2017-12-12 17:17:02 ] INFO Separating transcripts (UTR regions) per gene for SK3R12_Tdil22_11...
[ 2017-12-12 17:17:16 ] INFO Saving gene model to /data/images/proton2/run374/SKobold/metaseqr_quantseq_Tbil22VSTdil22/data/gene_model.RData
[ 2017-12-12 17:17:19 ] INFO Summarizing count data...
[ 2017-12-12 17:17:31 ] INFO Removing genes with zero counts in all samples...
[ 2017-12-12 17:17:31 ] INFO Normalizing with: deseq
[ 2017-12-12 17:17:31 ] INFO Applying gene filter length...
[ 2017-12-12 17:17:31 ] INFO Threshold below which ignored: 500
[ 2017-12-12 17:17:31 ] INFO Applying gene filter avg.reads...
[ 2017-12-12 17:17:31 ] INFO Threshold below which ignored: 0.0508323810471251
[ 2017-12-12 17:17:31 ] INFO Applying gene filter expression...
[ 2017-12-12 17:17:31 ] INFO Threshold below which ignored: 9
[ 2017-12-12 17:17:31 ] INFO Applying gene filter biotype...
[ 2017-12-12 17:17:31 ] INFO Biotypes ignored: rRNA, IG_V_pseudogene, TR_V_pseudogene
[ 2017-12-12 17:17:32 ] INFO 38925 genes filtered out
[ 2017-12-12 17:17:32 ] INFO 10623 genes remain after filtering
[ 2017-12-12 17:17:32 ] INFO Running statistical tests with: deseq
[ 2017-12-12 17:17:32 ] INFO Contrast: Tbil22_vs_Tdil22
[ 2017-12-12 17:17:37 ] INFO Contrast Tbil22_vs_Tdil22: found 905 genes
[ 2017-12-12 17:17:37 ] INFO Building output files...
[ 2017-12-12 17:17:41 ] INFO Contrast: Tbil22_vs_Tdil22
[ 2017-12-12 17:17:41 ] INFO Adding non-filtered data...
[ 2017-12-12 17:17:41 ] INFO binding annotation...
[ 2017-12-12 17:17:41 ] INFO binding p-values...
[ 2017-12-12 17:17:41 ] INFO binding FDRs...
[ 2017-12-12 17:17:41 ] INFO binding log2 normalized fold changes...
[ 2017-12-12 17:17:41 ] INFO binding normalized mean counts...
[ 2017-12-12 17:17:42 ] INFO binding normalized mean counts...
[ 2017-12-12 17:17:42 ] INFO binding all normalized counts for Tbil22...
[ 2017-12-12 17:17:42 ] INFO binding all normalized counts for Tdil22...
[ 2017-12-12 17:17:43 ] INFO binding filtering flags...
[ 2017-12-12 17:17:43 ] INFO Writing output...
[ 2017-12-12 17:17:43 ] INFO Adding filtered data...
[ 2017-12-12 17:17:43 ] INFO binding annotation...
[ 2017-12-12 17:17:44 ] INFO binding p-values...
[ 2017-12-12 17:17:44 ] INFO binding FDRs...
[ 2017-12-12 17:17:44 ] INFO binding log2 normalized fold changes...
[ 2017-12-12 17:17:44 ] INFO binding normalized mean counts...
[ 2017-12-12 17:17:45 ] INFO binding normalized mean counts...
[ 2017-12-12 17:17:45 ] INFO binding all normalized counts for Tbil22...
[ 2017-12-12 17:17:45 ] INFO binding all normalized counts for Tdil22...
[ 2017-12-12 17:17:45 ] INFO binding filtering flags...
[ 2017-12-12 17:17:46 ] INFO Writing output...
[ 2017-12-12 17:17:48 ] INFO Creating quality control graphs...
[ 2017-12-12 17:17:48 ] INFO Plotting in png format...
[ 2017-12-12 17:17:48 ] INFO Plotting mds...
[ 2017-12-12 17:17:48 ] INFO Plotting biodetection...
[ 2017-12-12 17:17:49 ] INFO Plotting countsbio...
[ 2017-12-12 17:17:50 ] INFO Plotting saturation...
[ 2017-12-12 17:17:54 ] INFO Plotting readnoise...
[ 2017-12-12 17:17:54 ] INFO Plotting correl...
[ 2017-12-12 17:17:55 ] INFO Plotting pairwise...
[ 2017-12-12 17:18:03 ] INFO Plotting boxplot...
[ 2017-12-12 17:18:03 ] INFO Plotting gcbias...
[ 2017-12-12 17:18:03 ] INFO Plotting lengthbias...
[ 2017-12-12 17:18:04 ] INFO Plotting boxplot...
[ 2017-12-12 17:18:04 ] INFO Plotting gcbias...
[ 2017-12-12 17:18:04 ] INFO Plotting lengthbias...
[ 2017-12-12 17:18:04 ] INFO Plotting biodist...
[ 2017-12-12 17:18:04 ] INFO Contrast: Tbil22_vs_Tdil22
[ 2017-12-12 17:18:05 ] INFO Plotting volcano...
[ 2017-12-12 17:18:05 ] INFO Contrast: Tbil22_vs_Tdil22
[ 2017-12-12 17:18:05 ] INFO Plotting deheatmap...
[ 2017-12-12 17:18:05 ] INFO Contrast: Tbil22_vs_Tdil22
[ 2017-12-12 17:18:26 ] INFO Plotting filtered...
[ 2017-12-12 17:18:26 ] INFO Plotting in pdf format...
[ 2017-12-12 17:18:26 ] INFO Plotting mds...
[ 2017-12-12 17:18:26 ] INFO Plotting biodetection...
[ 2017-12-12 17:18:26 ] INFO Plotting countsbio...
[ 2017-12-12 17:18:27 ] INFO Plotting saturation...
[ 2017-12-12 17:18:30 ] INFO Plotting readnoise...
[ 2017-12-12 17:18:31 ] INFO Plotting correl...
[ 2017-12-12 17:18:31 ] INFO Plotting pairwise...
[ 2017-12-12 17:18:35 ] INFO Plotting boxplot...
[ 2017-12-12 17:18:35 ] INFO Plotting gcbias...
[ 2017-12-12 17:18:36 ] INFO Plotting lengthbias...
[ 2017-12-12 17:18:37 ] INFO Plotting boxplot...
[ 2017-12-12 17:18:37 ] INFO Plotting gcbias...
[ 2017-12-12 17:18:37 ] INFO Plotting lengthbias...
[ 2017-12-12 17:18:38 ] INFO Plotting biodist...
[ 2017-12-12 17:18:38 ] INFO Contrast: Tbil22_vs_Tdil22
[ 2017-12-12 17:18:38 ] INFO Plotting volcano...
[ 2017-12-12 17:18:38 ] INFO Contrast: Tbil22_vs_Tdil22
[ 2017-12-12 17:18:38 ] INFO Plotting deheatmap...
[ 2017-12-12 17:18:38 ] INFO Contrast: Tbil22_vs_Tdil22
[ 2017-12-12 17:18:59 ] INFO Plotting filtered...
[ 2017-12-12 17:18:59 ] INFO Creating HTML report...
[ 2017-12-12 17:18:59 ] INFO Compressing figures...

Figures

Multidimensional scaling

The Multi-Dimensional Scaling (MDS) plots comprise a means of visualizing the level of similarity of individual cases of a dataset. It is similar to Principal Component Analysis (PCA), but instead of using the covariance matrix to find similarities among cases, MDS uses absolute distance metrics such as the classical Euclidean distance. Because of the relative linear relations among sequencing samples, it provides a more realistic clustering among samples. MDS serves quality control and it can be interpreted as follows: when the distance among samples of the same biological condition in the MDS space is small, this is an indication of high correlation and reproducibility among them. When this distance is larger or heterogeneous (e.g. the 3rd sample of a triplicate set is further from the other 2), this constitutes an indication of low correlation and reproducibility among samples. It can help exclude poor samples from further analysis.

Biotype detection

The biotype detection bar diagrams are a set of quality control charts that show the percentage of each biotype in the genome (i.e. in the whole set of features provided, for example, protein coding genes, non coding RNAs or pseudogenes) in grey bars, which proportion has been detected in a sample before normalization and after a basic filtering by removing features with zero counts in red lined bars, and the percentage of each biotype within the sample in solid red bars. The difference between grey bars and solid red bars is that the grey bars show the percentage of a feature in the genome while the solid red bars show the percentage in the sample. Thus, the solid red bars may be sometimes higher than the grey bars because certain features (e.g. protein coding genes) may be detected within a sample with a higher proportion relatively to their presence in the genome, as compared with other features. For example, while the percentage of protein coding genes in the whole genome is already higher than other biotypes, this percentage is expected to be even higher in an RNA-Seq experiment where one expects protein-coding genes to exhibit greater abundance. The vertical green line separates the most abundant biotypes (on the left-hand side, corresponding to the left axis scale) from the rest (on the right-hand side, corresponding to the right axis scale). Otherwise, the lower abundance biotypes would be indistinguishable. Unexpected outcomes in this quality control chart (e.g. very low detection of protein coding genes) would signify possible low quality of a sample.

Biotype detection counts

The biotype detection counts boxplots are a set of quality control charts that depict both the biological classification for the detected features and the actual distribution of the read counts for each biological type. The boxplot comprises a means of summarizing the read counts distribution of a sample in the form of a bar with extending lines, as commonly used way of graphically presenting groups of numerical data. A boxplot also indicates which observations, if any, might be considered outliers and is able to visually show different types of populations, without making any assumptions of the underlying statistical distribution. The spacing between the different parts of the box help indicate variance, skewness and identify outliers. The thick bar inside the colored box is the median of the observations while the box extends over the Interquartile Range of the observations. The whiskers extend up (down) to +/-1.5xIQR. Unexpected outcomes (e.g. protein coding read count distribution similar to pseudogene read count distribution) indicates poor sample quality.

Read and biotype saturation

The read and biotype saturation plots are a set of quality control charts that depict the read count saturation levels at several sequencing depths. Thus, they comprise a means of assessing whether the sequencing depth of an RNA-Seq experiment is sufficient in order to detect the biological features under investigation. These quality control charts are separated in two subgroups: the first subgroup (read saturation per biotype for all samples) is a set of plots, one for each biological feature (e.g. protein coding, pseudogene, lincRNA, etc.), that depict the number of detected features in different sequencing depths and for all samples in the same plot. The second subgroup (read saturation per sample for all biotypes) is a set of plots similar to the above, but with, there is one pair of plots with two panels for each sample, presenting all biological features. The left panel depicts the saturation levels for the less abundatnt features, while the right panel, the saturation for the more abundant features, as placing them all together would make the less abundant features indistinguishable. All the saturation plots should be interpreted as follows: if the read counts for a biotype tend to be saturated, the respective curve should tend to reach a plateau at higher depths. Otherwise, more sequencing is needed for the specific biotype.

Read saturation per biotype for all samples

Read saturation per sample for all biotypes

RNA-Seq reads noise

The read noise plots depict the percentage of biological features detected when subsampling the total number of reads. Very steep curves in read noise plots indicate that although the sequencing depth reaches its maximum, a relatively small percentage of total features is detected, indicating that the level of background noise is relatively high. Less steep RNA composition curves, indicate less noise. When a sample's curve deviate from the rest, it could indicate lower or higher quality, depending on the curves of the rest of the samples.

Correlation plots

The sample correlation plots depict the accordance among the RNA-Seq samples, as this is manifested through the read counts table used with the metaseqr pipeline, with two representations that both use the correlation matrix (a matrix which depicts all the pairwise correlations between each pair of samples) of the read counts matrix. The first is a correlation clustered heatmap which depicts the correlations among samples as color-scaled image and the hierarchical clustering tree depicts the grouping of the samples according to their correlation. Samples from the same group that are not clustered together provides an indication that there might be a quality problem with the dataset. The second is a 'correlogram' plot, where again the samples are hierarchically clustered and grouped but this time correlations are presented as ellipses inside each cell. Each cell represents a pairwise comparison and each correlation coefficient is represented by an ellipse whose 'diameter', direction and color depict the accordance for that pair of samples. Highly correlated samples are depicted as ellipses with narrow diameter, while poorly correlated samples are depicted as ellipses with wide diameters. Also, highly correlated samples are depicted as ellipses with a left-to-right upwards direction while poorly correlated samples are depicted as ellipses with a right-to-left upwards direction.

Correlation heatmap

Data correlogram

Pairwise scatterplots

The pairwise comparison plots are split in three parts: the upper diagonal consists of simple scatterplots for all pairwise sample comparisons, together with their Pearson correlation coefficient. It is a simple measure of between sample correlation using all the available data points instead of only the correlation matrix. The lower diagonal consists of mean-difference plots for all pairwise sample comparisons. A mean-difference plot (or a Bland-Altman plots) is a method of data plotting used in analyzing the agreement between two different assays/variables. In this graphical method the differences (or alternatively the ratios) between the two variables are plotted against the averages of the two. Such a plot is useful, for example, for analyzing data with strong correlation between x and y axes, when the (x,y) dots on the plot are close to the diagonal x=y. In this case, the value of the transformed variable X is about the same as x and y and the variable Y shows the difference between x and y. In both represantations, irregular shapes of the red smoother lines are an indication of poor correlation between samples or of other systematic bias sources, which is usually corrected through data normalization.

Boxplots

The boxplot comprises a means of summarizing the read counts distribution of a sample in the form of a bar with extending lines, as a commonly used way of graphically presenting groups of numerical data. A boxplot also indicates which observations, if any, might be considered outliers and is able to visually show different types of populations, without making any assumptions about the underlying statistical distribution. The spacings between the different parts of the box help indicate variance, skewness and identify outliers. The thick bar inside the colored box is the median of the observations while the box extends over the Interquartile Range of the observations. The whiskers extend up (down) to +/-1.5xIQR. Boxplots at similar levels indicate good quality of the normalization. If boxplots remain at different levels after normalization, maybe another normalization algorithm may have to be examined. The un-normalized boxplots show the need for data normalization in order for the data from different samples to follow the same underlying distribution and statistical testing becoming possible.

Boxplot of un-normalized data

Boxplot of normalized data

GC content bias

The GC-content bias plot is a quality control chart that shows the possible dependence of the read counts (in log2 scale) under a gene to the GC content percentage of that gene. In order for the statistical tests to be able to detect statistical significance which occurs due to real biological effects and not by other systematic biases present in the data (e.g. a possible GC-content bias), the latter should be accounted for by the applied normalization algorithm. Although the tests are performed for each gene across biological conditions one could assume that the GC content does not represent a bias, as it is the same for the tested gene across samples and conditions. However, Risso et al. (2011) showed that the GC-content could have an impact in the statistical testing procedure. The GC-content bias plot depicts the dependence of the read counts to the GC content before and after normalization. The smoothing lines for each sample, should be as 'straight' as possible after normalization. In addition, if the smoothing lines differ significantly between biological conditions, this would constitute a possible quality warning.

GC content bias un-normalized

GC content bias normalized

Gene/transcript length bias

The gene/transcript length bias plot is a quality control chart that shows the possible dependence of the read counts (in log2 scale) under a gene to the length that gene (whole gene or sum of exons depending on the analysis). In order for the statistical tests to be able to detect statistical significance which occurs due to real biological effects and not by other systematic biases present in the data (e.g. a possible length bias), the latter should be accounted for by the applied normalization algorithm. Although the tests are performed for each gene across bioogical conditions, one could assume that the gene length does not represent a bias as it's the same for the tested gene across samples and conditions. However, it has been shown in several studies that the gene length could have an impact on the statistical testing procedure. The length bias plot depicts the dependence of the read counts to the gene/transcript length before and after normalization. The smoothing lines for each sample, should be as 'straight' as possible after normalization. In addition, if the smoothing lines differ significantly among biological conditions, this would constitute a possible quality warning.

Gene/transcript length bias un-normalized

Gene/transcript length bias normalized

Chromosome and biotype distribution of filtered genes

The chromosome and biotype distribution of filtered genes is a quality control chart with two rows and four panels: on the left panel of the first row, the bar chart depicts the numbers of filtered genes per chromosome (actual numbers shown above the bars). On the right panel of the first row, the bar chart depicts the numbers of filtered genes per biotype (actual numbers shown above the bars). On the left panel of the second row, the bar chart depicts the fraction of the filtered genes to the total genes per chromosome (actual percentages shown above the bars). On the right panel of the second row, the bar chart depicts the fraction of the filtered genes to the total genes per biotype (actual percentages shown above the bars). This plot should indicate possible quality problems when for example the filtered genes for a specific chromosome (or the fraction) is extremely higher than the rest. Generally, the fractions per chromosome should be uniform and the fractions per biotype should be proportional to the biotype fraction relative to the genome.

Volcano plots

A volcano plot is a scatterplot that is often used when analyzing high-throughput -omics data (e.g. microarray data, RNA-Seq data) to give an overview of interesting genes. The log2 fold change is plotted on the x-axis and the negative log10 p-value is plotted on the y-axis. A volcano plot combines the results of a statistical test (aka, p-values) with the magnitude of the change enabling quick visual identification of those genes that display large-magnitude changes that are also statistically significant. The horizontal dashed line sets the threshold for statistical significance, while the vertical dashed lines set the thresholds for biological significance. It should be noted that the volcano plots become harder to interpret when using more than one statistical algorithm and performing meta-analysis. This happens because the genes that have stronger evidence of being differentially expressed obtain lower p-values while the rest either remain at similar levels or obtain higher p-values. The result is a 'warped' volcano plot, with two main data clouds: one in the upper part of the plot, and one in the lower part of the plot. You can always zoom in when using interacting mode (the default).

Volcano plot for the contrast Tbil22_vs_Tdil22

DEG heatmaps

The Differentially Expressed Genes (DEGs) heatmaps depict how well samples from different conditions cluster together according to their expression values after normalization and statistical testing, for each requested statistical contrast. If samples from the same biological condition do not cluster together, this would constitute a warning sign regarding the quality of the samples. In addition, DEG heatmaps provide an initial view of possible clusters of co-expressed genes.

DEG heatmap for the contrast Tbil22_vs_Tdil22

Chromosome and biotype distributions of DEGs

The chromosome and biotype distributions bar diagram for Differentially Expressed Genes (DEGs) is split in two panels: i) on the left panel DEGs are distributed per chromosome and the percentage of each chromosome in the genome is presented in grey bars, the percentage of DEGs in each chromosome is presented in red lined bars and the percentage of certain chromosomes in the distribution of DEGs is presented in solid red bars. ii) on the right panel, DEGs are distributed per biotype and the percentage of each biotype in the genome (i.e. in the whole set of features provided, for example, protein coding genes, non coding RNAs or pseudogenes) is presented in grey bars, the percentage of DEGs in each biotype is presented in blue lined bars and the percentage of each biotype in DEGs is presented in solid blue lines. The vertical green line separates the most abundant biotypes (on the left-hand side, corresponding to the left axis scale), from the rest (on the right-hand side, corresponding to the right axis scale). Otherwise, the lower abundance, biotypes would be indistinguishable.

Biotype distribution in DEG for the contrast Tbil22_vs_Tdil22

Get all the figures in png, pdf format.

Results

DEG table for the contrast Tbil22_vs_Tdil22

The following table presents the top 10% statistically significant genes (use the download links below the table to retrieve the whole list) for the contrast Tbil22_vs_Tdil22. The fields of the table correspond to the requested features to be exported. The table can be searched using the search field on the top right.

chromosome	start	end	gene_id	gc_content	strand	gene_name	biotype	p-value_deseq	FDR_deseq	log2_normalized_fold_change_Tbil22_vs_Tdil22	log2_normalized_mean_counts_Tbil22	rpgm_normalized_mean_counts_Tbil22	log2_normalized_mean_counts_Tdil22	rpgm_normalized_mean_counts_Tdil22	log2_normalized_counts_SK3R4_Tbil22	log2_normalized_counts_SK3R5_Tbil22	log2_normalized_counts_SK3R6_Tbil22	rpgm_normalized_counts_SK3R4_Tbil22	rpgm_normalized_counts_SK3R5_Tbil22	rpgm_normalized_counts_SK3R6_Tbil22	log2_normalized_counts_SK3R10_Tdil22_9	log2_normalized_counts_SK3R11_Tdil22_10	log2_normalized_counts_SK3R12_Tdil22_11	rpgm_normalized_counts_SK3R10_Tdil22_9	rpgm_normalized_counts_SK3R11_Tdil22_10	rpgm_normalized_counts_SK3R12_Tdil22_11
chr3	88542029	88548300	ENSMUSG00000008601	0.5049	-	Rab25	protein_coding	3.35e-15	3.56e-11	5.24634	2.94e+00	1.73e-03	7.72e+00	7.49e-02	3.17e+00	2.32e+00	3.32e+00	1.98e-03	9.90e-04	2.23e-03	9.12e+00	8.22e+00	5.81e+00	1.37e-01	7.37e-02	1.36e-02
chr12	26442753	26456452	ENSMUSG00000020641	0.4351	-	Rsad2	protein_coding	1.43e-14	7.58e-11	-5.29921	6.69e+00	1.15e-02	1.11e+00	1.84e-04	7.00e+00	6.71e+00	6.36e+00	1.41e-02	1.15e-02	8.96e-03	1.00e+00	2.32e+00	0.00e+00	1.11e-04	4.43e-04	0.00e+00
chrX	74135133	74136187	ENSMUSG00000081118	0.4578	-	Gm6758	processed_pseudogene	4.24e-13	1.50e-09	-4.37218	5.55e+00	4.34e-01	3.42e+00	2.02e-02	3.17e+00	1.07e+01	2.81e+00	6.38e-03	1.29e+00	4.78e-03	6.09e+00	3.17e+00	1.00e+00	5.34e-02	6.38e-03	7.97e-04
chr7	65861734	66050386	ENSMUSG00000030513	0.4727	+	Pcsk6	protein_coding	1.63e-12	4.34e-09	5.42626	3.33e-01	5.21e-05	5.34e+00	8.80e-03	0.00e+00	0.00e+00	1.00e+00	0.00e+00	0.00e+00	1.56e-04	5.67e+00	6.77e+00	3.58e+00	7.81e-03	1.69e-02	1.72e-03
chr17	34589806	34591754	ENSMUSG00000034786	0.5813	+	Gpsm3	protein_coding	1.75e-11	3.71e-08	-5.22882	5.60e+00	1.64e-02	3.33e-01	1.11e-04	5.29e+00	5.43e+00	6.09e+00	1.27e-02	1.40e-02	2.24e-02	1.00e+00	0.00e+00	0.00e+00	3.34e-04	0.00e+00	0.00e+00
chr1	51289126	51302960	ENSMUSG00000045954	0.4193	+	Sdpr	protein_coding	3.94e-11	6.97e-08	-4.48113	6.48e+00	3.16e-02	1.86e+00	1.07e-03	6.57e+00	6.44e+00	6.43e+00	3.37e-02	3.08e-02	3.04e-02	2.00e+00	1.00e+00	2.58e+00	1.07e-03	3.58e-04	1.79e-03
chr1	79794197	79861180	ENSMUSG00000026249	0.4461	-	Serpine2	protein_coding	8.03e-11	1.22e-07	-5.27302	5.26e+00	4.52e-03	0.00e+00	0.00e+00	5.29e+00	5.04e+00	5.46e+00	4.56e-03	3.84e-03	5.16e-03	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr4	111807358	111808423	ENSMUSG00000100514	0.4550	-	Gm12960	processed_pseudogene	1.04e-10	1.38e-07	-3.58668	6.26e+00	2.23e-01	4.49e+00	1.78e-02	5.25e+00	9.62e+00	3.91e+00	2.95e-02	6.28e-01	1.12e-02	5.04e+00	4.25e+00	4.17e+00	2.55e-02	1.44e-02	1.36e-02
chr5	92346638	92348889	ENSMUSG00000034855	0.4720	-	Cxcl10	protein_coding	1.36e-10	1.60e-07	-4.50779	5.85e+00	2.28e-02	8.62e-01	6.37e-04	6.02e+00	6.32e+00	5.21e+00	2.45e-02	3.02e-02	1.38e-02	2.58e+00	0.00e+00	0.00e+00	1.91e-03	0.00e+00	0.00e+00
chr11	83525778	83530518	ENSMUSG00000035042	0.4664	-	Ccl5	protein_coding	1.88e-10	1.96e-07	-4.30875	6.13e+00	2.65e-02	1.60e+00	9.85e-04	6.63e+00	6.04e+00	5.73e+00	3.62e-02	2.40e-02	1.92e-02	1.00e+00	2.81e+00	1.00e+00	3.69e-04	2.22e-03	3.69e-04
chr8	33782643	33929863	ENSMUSG00000031586	0.4657	-	Rbpms	protein_coding	2.03e-10	1.96e-07	-5.18322	5.12e+00	3.57e-03	0.00e+00	0.00e+00	4.75e+00	4.91e+00	5.70e+00	2.63e-03	2.93e-03	5.15e-03	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr8	112013433	112015036	ENSMUSG00000092086	0.4383	-	Gm6793	protein_coding	3.23e-10	2.86e-07	-3.63834	7.91e+00	6.58e-01	6.24e+00	5.22e-02	6.69e+00	1.15e+01	5.55e+00	6.71e-02	1.88e+00	3.03e-02	6.85e+00	5.61e+00	6.27e+00	7.50e-02	3.16e-02	5.00e-02
chr11	100203162	100207548	ENSMUSG00000045545	0.5250	-	Krt14	protein_coding	3.61e-10	2.95e-07	4.06434	4.61e+00	1.69e-02	8.66e+00	2.93e-01	4.39e+00	4.52e+00	4.91e+00	1.42e-02	1.57e-02	2.07e-02	8.76e+00	8.95e+00	8.27e+00	3.08e-01	3.52e-01	2.19e-01
chr11	82035571	82037453	ENSMUSG00000035385	0.4376	+	Ccl2	protein_coding	6.75e-10	5.12e-07	-4.19878	6.04e+00	4.34e-02	1.55e+00	1.74e-03	5.58e+00	6.30e+00	6.23e+00	3.07e-02	5.10e-02	4.84e-02	2.32e+00	0.00e+00	2.32e+00	2.62e-03	0.00e+00	2.62e-03
chr15	54571366	54602846	ENSMUSG00000024479	0.4201	+	Mal2	protein_coding	8.98e-10	6.36e-07	4.20645	2.06e+00	9.93e-04	6.32e+00	2.35e-02	2.58e+00	2.00e+00	1.58e+00	1.49e-03	8.93e-04	5.96e-04	6.13e+00	6.43e+00	6.39e+00	2.05e-02	2.53e-02	2.47e-02
chr12	81372213	81373221	ENSMUSG00000067224	0.5471	-	Gm3695	processed_pseudogene	2.80e-09	1.86e-06	3.73272	2.15e+00	3.72e-03	4.37e+00	5.93e-02	1.00e+00	2.00e+00	3.46e+00	7.97e-04	2.39e-03	7.97e-03	7.66e+00	4.46e+00	1.00e+00	1.60e-01	1.67e-02	7.97e-04
chr6	113458484	113470758	ENSMUSG00000043088	0.4934	+	Il17re	protein_coding	3.32e-09	2.07e-06	4.34239	1.00e+00	7.46e-05	5.12e+00	2.59e-03	1.00e+00	2.00e+00	0.00e+00	5.59e-05	1.68e-04	0.00e+00	6.66e+00	4.52e+00	4.17e+00	5.59e-03	1.23e-03	9.51e-04
chr5	16729996	16731070	ENSMUSG00000059179	0.4502	-	Gm8991	processed_pseudogene	7.05e-09	4.16e-06	-3.25307	7.71e+00	5.97e-01	6.05e+00	6.19e-02	6.43e+00	1.10e+01	5.64e+00	6.78e-02	1.69e+00	3.91e-02	7.20e+00	5.32e+00	5.61e+00	1.16e-01	3.11e-02	3.83e-02
chr13	23738808	23740367	ENSMUSG00000036181	0.5506	+	Hist1h1c	protein_coding	1.21e-08	6.74e-06	3.55180	2.86e+00	3.37e-03	5.59e+00	4.52e-02	3.00e+00	3.00e+00	2.58e+00	3.73e-03	3.73e-03	2.66e-03	7.73e+00	4.64e+00	4.39e+00	1.12e-01	1.28e-02	1.06e-02
chr14	122117938	122119009	ENSMUSG00000066538	0.4552	+	Gm6254	processed_pseudogene	1.86e-08	9.75e-06	-3.02680	5.11e+00	8.59e-02	3.65e+00	9.84e-03	3.91e+00	8.24e+00	3.17e+00	1.12e-02	2.40e-01	6.38e-03	4.32e+00	3.46e+00	3.17e+00	1.52e-02	7.97e-03	6.38e-03
chr5	114923240	114937915	ENSMUSG00000041827	0.4958	+	Oasl1	protein_coding	1.93e-08	9.75e-06	-4.04439	5.67e+00	1.24e-02	1.64e+00	5.36e-04	5.43e+00	5.13e+00	6.44e+00	9.65e-03	7.81e-03	1.98e-02	2.32e+00	1.00e+00	1.58e+00	9.19e-04	2.30e-04	4.60e-04
chr2	164354070	164389095	ENSMUSG00000017002	0.4508	-	Slpi	protein_coding	2.74e-08	1.32e-05	-3.34783	8.83e+00	1.25e-01	5.51e+00	1.20e-02	8.54e+00	8.67e+00	9.29e+00	9.91e-02	1.08e-01	1.66e-01	5.25e+00	5.75e+00	5.52e+00	9.86e-03	1.41e-02	1.20e-02
chr12	113146316	113153879	ENSMUSG00000006360	0.5500	+	Crip1	protein_coding	3.15e-08	1.45e-05	-3.47050	7.51e+00	1.47e-01	4.28e+00	1.27e-02	7.24e+00	6.63e+00	8.66e+00	1.02e-01	6.64e-02	2.73e-01	4.58e+00	4.25e+00	4.00e+00	1.56e-02	1.22e-02	1.02e-02
chr8	68276567	68347699	ENSMUSG00000053886	0.4190	+	Sh2d4a	protein_coding	3.49e-08	1.55e-05	4.23266	5.28e-01	3.02e-04	4.86e+00	1.37e-02	0.00e+00	0.00e+00	1.58e+00	0.00e+00	0.00e+00	9.06e-04	4.86e+00	5.55e+00	4.17e+00	1.27e-02	2.08e-02	7.70e-03
chr14	70164218	70177681	ENSMUSG00000022090	0.5359	-	Pdlim2	protein_coding	3.76e-08	1.60e-05	-3.24703	8.02e+00	4.86e-02	4.75e+00	4.95e-03	8.45e+00	8.09e+00	7.52e+00	6.32e-02	4.93e-02	3.32e-02	4.32e+00	4.58e+00	5.36e+00	3.44e-03	4.17e-03	7.25e-03
chr8	84125989	84132527	ENSMUSG00000012889	0.5512	+	Podnl1	protein_coding	4.06e-08	1.66e-05	-3.57862	6.22e+00	4.80e-02	2.30e+00	3.43e-03	6.38e+00	5.91e+00	6.39e+00	5.28e-02	3.80e-02	5.34e-02	2.32e+00	1.00e+00	3.58e+00	2.57e-03	6.44e-04	7.08e-03
chr15	102215995	102231935	ENSMUSG00000001281	0.4953	-	Itgb7	protein_coding	6.44e-08	2.53e-05	-4.54432	4.54e+00	8.36e-03	0.00e+00	0.00e+00	4.52e+00	4.70e+00	4.39e+00	8.23e-03	9.35e-03	7.48e-03	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr11	63128982	63159547	ENSMUSG00000018217	0.4813	+	Pmp22	protein_coding	6.83e-08	2.59e-05	-3.88753	5.59e+00	1.33e-02	1.72e+00	6.41e-04	5.95e+00	5.17e+00	5.64e+00	1.68e-02	9.62e-03	1.35e-02	1.58e+00	1.58e+00	2.00e+00	5.49e-04	5.49e-04	8.24e-04
chr1	36264597	36274679	ENSMUSG00000047180	0.5155	-	Neurl3	protein_coding	7.83e-08	2.87e-05	-3.19094	7.33e+00	3.52e-02	4.13e+00	3.66e-03	7.30e+00	7.20e+00	7.48e+00	3.45e-02	3.21e-02	3.91e-02	4.17e+00	4.32e+00	3.91e+00	3.73e-03	4.17e-03	3.08e-03
chr10	18581839	18743949	ENSMUSG00000019852	0.4181	-	Arfgef3	protein_coding	9.00e-08	3.15e-05	3.37851	2.06e+00	4.36e-04	5.68e+00	5.56e-03	2.00e+00	1.00e+00	3.17e+00	3.27e-04	1.09e-04	8.73e-04	6.04e+00	5.43e+00	5.55e+00	7.09e-03	4.58e-03	5.02e-03
chr8	3587293	3609075	ENSMUSG00000044433	0.5165	+	Camsap3	protein_coding	9.20e-08	3.15e-05	4.52356	0.00e+00	0.00e+00	4.39e+00	3.52e-03	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	3.58e+00	5.13e+00	4.46e+00	1.76e-03	5.45e-03	3.36e-03
chr2	24993357	24994343	ENSMUSG00000083796	0.4711	-	Gm13369	processed_pseudogene	1.31e-07	4.35e-05	-2.74250	6.90e+00	2.07e-01	5.24e+00	3.03e-02	6.00e+00	9.41e+00	5.29e+00	5.02e-02	5.41e-01	3.03e-02	5.73e+00	5.00e+00	5.00e+00	4.15e-02	2.47e-02	2.47e-02
chr7	59974149	60005064	ENSMUSG00000000948	0.3542	-	Gm38393	protein_coding	1.66e-07	5.33e-05	3.32617	3.47e+00	1.04e-03	6.61e+00	1.13e-02	3.00e+00	3.70e+00	3.70e+00	7.02e-04	1.20e-03	1.20e-03	6.81e+00	7.52e+00	5.49e+00	1.11e-02	1.84e-02	4.42e-03
chr7	59982495	60140219	ENSMUSG00000102252	0.3711	-	Snrpn	protein_coding	1.85e-07	5.79e-05	3.30914	3.47e+00	5.17e-03	6.56e+00	5.57e-02	3.00e+00	3.70e+00	3.70e+00	3.50e-03	6.00e-03	6.00e-03	6.81e+00	7.52e+00	5.36e+00	5.55e-02	9.15e-02	2.00e-02
chr8	23411502	23449632	ENSMUSG00000031548	0.4776	+	Sfrp1	protein_coding	2.42e-07	7.34e-05	3.88536	1.53e+00	4.55e-04	5.35e+00	9.85e-03	1.00e+00	2.00e+00	1.58e+00	2.27e-04	6.82e-04	4.55e-04	5.73e+00	5.86e+00	4.46e+00	1.18e-02	1.30e-02	4.77e-03
chr2	30889471	30929863	ENSMUSG00000050737	0.4993	-	Ptges	protein_coding	3.39e-07	9.99e-05	-3.43296	5.11e+00	8.03e-03	1.64e+00	5.35e-04	4.95e+00	5.67e+00	4.70e+00	6.88e-03	1.15e-02	5.73e-03	1.00e+00	2.32e+00	1.58e+00	2.29e-04	9.17e-04	4.59e-04
chrX	102247381	102252181	ENSMUSG00000051159	0.5734	-	Cited1	protein_coding	3.57e-07	1.00e-04	3.59296	2.19e+00	1.45e-03	5.83e+00	2.15e-02	2.00e+00	3.00e+00	1.58e+00	1.09e-03	2.54e-03	7.26e-04	6.49e+00	5.39e+00	5.61e+00	3.23e-02	1.49e-02	1.74e-02
chr14	69029289	69041401	ENSMUSG00000014813	0.4445	+	Stc1	protein_coding	3.58e-07	1.00e-04	-3.39559	6.01e+00	1.75e-02	2.38e+00	1.45e-03	5.46e+00	5.52e+00	7.03e+00	1.04e-02	1.08e-02	3.13e-02	3.81e+00	2.32e+00	1.00e+00	3.13e-03	9.63e-04	2.41e-04
chr19	34640871	34650009	ENSMUSG00000034459	0.4291	+	Ifit1	protein_coding	4.67e-07	1.27e-04	-4.36923	4.35e+00	8.17e-03	0.00e+00	0.00e+00	4.09e+00	4.32e+00	4.64e+00	6.65e-03	7.90e-03	9.98e-03	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr3	61461859	61462861	ENSMUSG00000098208	0.5474	-	Gm8349	processed_pseudogene	5.48e-07	1.45e-04	2.67076	4.24e+00	1.73e-02	6.17e+00	1.14e-01	3.70e+00	3.58e+00	5.43e+00	9.57e-03	8.77e-03	3.35e-02	8.38e+00	6.43e+00	3.70e+00	2.66e-01	6.78e-02	9.57e-03
chr7	65784859	65785932	ENSMUSG00000068480	0.4544	+	Gm7551	processed_pseudogene	6.02e-07	1.54e-04	-2.65124	4.86e+00	6.43e-02	3.59e+00	9.57e-03	3.32e+00	7.81e+00	3.46e+00	7.18e-03	1.78e-01	7.97e-03	4.25e+00	2.81e+00	3.70e+00	1.44e-02	4.78e-03	9.57e-03
chr2	27886425	28039514	ENSMUSG00000026837	0.5115	+	Col5a1	protein_coding	6.09e-07	1.54e-04	-4.14975	4.51e+00	6.04e-03	3.33e-01	8.88e-05	4.95e+00	4.58e+00	4.00e+00	7.99e-03	6.13e-03	4.00e-03	0.00e+00	0.00e+00	1.00e+00	0.00e+00	0.00e+00	2.66e-04
chr16	23520291	23614222	ENSMUSG00000033355	0.4257	+	Rtp4	protein_coding	7.78e-07	1.88e-04	-3.23840	5.61e+00	2.10e-02	2.41e+00	1.84e-03	5.95e+00	5.75e+00	5.13e+00	2.59e-02	2.25e-02	1.44e-02	2.32e+00	2.32e+00	2.58e+00	1.70e-03	1.70e-03	2.12e-03
chr8	121054882	121083110	ENSMUSG00000097336	0.5216	-	Fendrr	processed_transcript	7.80e-07	1.88e-04	-3.65025	5.01e+00	9.75e-03	1.30e+00	5.32e-04	4.09e+00	4.91e+00	6.04e+00	4.25e-03	7.71e-03	1.73e-02	2.32e+00	0.00e+00	1.58e+00	1.06e-03	0.00e+00	5.32e-04
chr4	156199424	156200818	ENSMUSG00000035692	0.5068	-	Isg15	protein_coding	9.27e-07	2.19e-04	-2.82166	6.52e+00	7.10e-02	3.72e+00	9.43e-03	7.09e+00	6.92e+00	5.55e+00	9.55e-02	8.49e-02	3.25e-02	3.00e+00	4.46e+00	3.70e+00	4.95e-03	1.49e-02	8.49e-03
chr3	90526856	90528837	ENSMUSG00000042306	0.5303	+	S100a14	protein_coding	1.12e-06	2.58e-04	3.79586	1.44e+00	1.07e-03	5.37e+00	2.17e-02	1.00e+00	2.32e+00	1.00e+00	5.33e-04	2.13e-03	5.33e-04	5.13e+00	5.49e+00	5.49e+00	1.81e-02	2.35e-02	2.35e-02
chr7	44549616	44553951	ENSMUSG00000060601	0.5888	-	Nr1h2	protein_coding	1.35e-06	3.06e-04	2.77879	4.08e+00	2.21e-03	6.18e+00	1.60e-02	4.25e+00	4.09e+00	3.91e+00	2.49e-03	2.21e-03	1.94e-03	8.05e+00	5.95e+00	4.52e+00	3.65e-02	8.43e-03	3.04e-03
chr12	78842989	78861638	ENSMUSG00000021114	0.4137	-	Atp6v1d	protein_coding	1.39e-06	3.08e-04	2.77781	7.14e+00	8.48e-02	8.60e+00	5.85e-01	6.87e+00	6.79e+00	7.75e+00	6.69e-02	6.34e-02	1.24e-01	1.14e+01	7.71e+00	6.69e+00	1.58e+00	1.20e-01	5.88e-02
chr11	26786633	26787634	ENSMUSG00000069939	0.5509	+	Gm12070	processed_pseudogene	1.86e-06	4.02e-04	2.85061	7.56e+00	1.54e-01	9.45e+00	1.12e+00	7.26e+00	7.38e+00	8.03e+00	1.21e-01	1.32e-01	2.08e-01	1.18e+01	8.87e+00	7.69e+00	2.81e+00	3.72e-01	1.64e-01
chr7	141262706	141266481	ENSMUSG00000025498	0.5482	-	Irf7	protein_coding	3.58e-06	7.43e-04	-3.88753	4.30e+00	3.69e-03	5.28e-01	1.04e-04	5.58e+00	4.00e+00	3.32e+00	7.33e-03	2.34e-03	1.40e-03	0.00e+00	0.00e+00	1.58e+00	0.00e+00	0.00e+00	3.12e-04
chrX	9009027	9010077	ENSMUSG00000083468	0.4577	-	Gm9431	processed_pseudogene	3.62e-06	7.43e-04	-2.77052	4.58e+00	3.00e-02	2.39e+00	3.72e-03	4.09e+00	6.49e+00	3.17e+00	1.28e-02	7.10e-02	6.38e-03	2.58e+00	1.58e+00	3.00e+00	3.99e-03	1.59e-03	5.58e-03
chr5	114896936	114912234	ENSMUSG00000029561	0.4664	+	Oasl2	protein_coding	3.71e-06	7.43e-04	-3.37851	5.06e+00	2.95e-03	1.64e+00	2.04e-04	5.58e+00	5.00e+00	4.58e+00	4.11e-03	2.71e-03	2.01e-03	2.32e+00	1.58e+00	1.00e+00	3.50e-04	1.75e-04	8.76e-05
chr2	75659261	75669407	ENSMUSG00000059005	0.4065	+	Hnrnpa3	protein_coding	3.73e-06	7.43e-04	-2.64785	1.02e+01	4.41e-01	8.76e+00	7.02e-02	8.98e+00	1.29e+01	8.73e+00	7.60e-02	1.18e+00	6.38e-02	9.52e+00	8.43e+00	8.32e+00	1.10e-01	5.20e-02	4.81e-02
chr2	36230426	36252272	ENSMUSG00000047250	0.4850	+	Ptgs1	protein_coding	3.78e-06	7.43e-04	-2.97124	5.58e+00	8.20e-03	2.53e+00	8.98e-04	5.09e+00	6.07e+00	5.58e+00	5.56e-03	1.11e-02	7.92e-03	3.00e+00	1.58e+00	3.00e+00	1.18e-03	3.37e-04	1.18e-03
chr4	11331933	11386783	ENSMUSG00000040728	0.4323	-	Esrp1	protein_coding	4.39e-06	8.47e-04	2.78196	5.44e+00	5.08e-03	8.19e+00	3.57e-02	5.46e+00	5.64e+00	5.21e+00	5.12e-03	5.84e-03	4.29e-03	8.64e+00	8.10e+00	7.84e+00	4.73e-02	3.25e-02	2.72e-02
chr3	142962611	142963612	ENSMUSG00000078162	0.5509	+	Gm2574	processed_pseudogene	4.70e-06	8.81e-04	2.50310	5.48e+00	3.69e-02	7.36e+00	2.13e-01	5.09e+00	5.21e+00	6.15e+00	2.63e-02	2.87e-02	5.58e-02	9.29e+00	6.92e+00	5.88e+00	4.98e-01	9.57e-02	4.63e-02
chr7	105895139	105953967	ENSMUSG00000078606	0.3969	-	Gm4070	protein_coding	4.73e-06	8.81e-04	-3.76553	4.50e+00	4.18e-03	6.67e-01	1.29e-04	4.46e+00	4.39e+00	4.64e+00	4.05e-03	3.86e-03	4.63e-03	1.00e+00	0.00e+00	1.00e+00	1.93e-04	0.00e+00	1.93e-04
chr5	137061504	137072268	ENSMUSG00000037411	0.5016	-	Serpine1	protein_coding	5.10e-06	9.29e-04	-2.57477	7.09e+00	3.35e-02	4.52e+00	5.43e-03	7.47e+00	7.23e+00	6.58e+00	4.21e-02	3.57e-02	2.27e-02	4.25e+00	5.00e+00	4.32e+00	4.31e-03	7.42e-03	4.55e-03
chr3	108012255	108017973	ENSMUSG00000058135	0.5130	-	Gstm1	protein_coding	5.21e-06	9.29e-04	2.80735	3.10e+00	4.94e-03	5.36e+00	3.81e-02	2.58e+00	2.81e+00	3.91e+00	2.96e-03	3.55e-03	8.29e-03	6.71e+00	6.38e+00	3.00e+00	6.16e-02	4.86e-02	4.15e-03
chr7	100760805	100761806	ENSMUSG00000097388	0.5449	-	Gm3200	processed_pseudogene	5.28e-06	9.29e-04	2.33131	4.74e+00	2.37e-02	6.62e+00	1.22e-01	4.75e+00	3.81e+00	5.67e+00	2.07e-02	1.04e-02	3.99e-02	8.45e+00	6.29e+00	5.13e+00	2.78e-01	6.14e-02	2.71e-02
chr17	72918305	72941942	ENSMUSG00000024063	0.4696	+	Lbh	protein_coding	5.33e-06	9.29e-04	2.65983	4.96e+00	6.25e-03	7.06e+00	4.05e-02	4.81e+00	4.58e+00	5.49e+00	5.38e-03	4.58e-03	8.77e-03	7.74e+00	8.53e+00	4.91e+00	4.25e-02	7.34e-02	5.78e-03
chr2	25706739	25707721	ENSMUSG00000049086	0.5799	+	Bmyc	protein_coding	6.25e-06	1.07e-03	3.14505	2.06e+00	2.60e-03	4.95e+00	2.91e-02	2.58e+00	2.00e+00	1.58e+00	3.90e-03	2.34e-03	1.56e-03	5.04e+00	6.11e+00	3.70e+00	2.50e-02	5.30e-02	9.36e-03
chr3	151730922	151749960	ENSMUSG00000028037	0.3843	-	Ifi44	protein_coding	6.57e-06	1.11e-03	-3.72247	4.46e+00	5.11e-03	5.28e-01	1.62e-04	4.46e+00	4.58e+00	4.32e+00	5.11e-03	5.59e-03	4.62e-03	1.58e+00	0.00e+00	0.00e+00	4.86e-04	0.00e+00	0.00e+00
chr4	12232833	12233951	ENSMUSG00000060989	0.4754	+	Gm11847	processed_pseudogene	7.29e-06	1.21e-03	-2.25584	5.76e+00	8.43e-02	4.43e+00	1.70e-02	5.43e+00	8.04e+00	3.81e+00	3.35e-02	2.09e-01	1.04e-02	4.39e+00	4.00e+00	4.91e+00	1.59e-02	1.20e-02	2.31e-02
chr15	36770770	36794547	ENSMUSG00000022285	0.4365	-	Ywhaz	protein_coding	7.77e-06	1.25e-03	3.38998	1.01e+01	1.84e-01	1.25e+01	1.93e+00	1.01e+01	1.01e+01	1.00e+01	1.89e-01	1.89e-01	1.74e-01	1.39e+01	1.41e+01	9.56e+00	2.59e+00	3.08e+00	1.28e-01
chr6	88724412	88828360	ENSMUSG00000033174	0.5082	+	Mgll	protein_coding	7.79e-06	1.25e-03	3.66297	8.62e-01	8.63e-05	4.57e+00	2.10e-03	1.00e+00	1.58e+00	0.00e+00	8.63e-05	1.73e-04	0.00e+00	5.25e+00	4.00e+00	4.46e+00	3.19e-03	1.29e-03	1.81e-03
chr19	34607970	34613401	ENSMUSG00000062488	0.4273	+	Ifit3b	protein_coding	8.31e-06	1.32e-03	-2.91254	5.24e+00	2.06e-02	2.46e+00	2.31e-03	5.93e+00	5.61e+00	4.17e+00	2.97e-02	2.38e-02	8.42e-03	2.58e+00	2.81e+00	2.00e+00	2.48e-03	2.97e-03	1.49e-03
chr7	31068172	31076704	ENSMUSG00000057092	0.5179	-	Fxyd3	protein_coding	8.78e-06	1.37e-03	3.56071	6.67e-01	8.63e-05	4.21e+00	2.42e-03	0.00e+00	1.00e+00	1.00e+00	0.00e+00	1.29e-04	1.29e-04	4.91e+00	3.81e+00	3.91e+00	3.75e-03	1.68e-03	1.81e-03
chr10	59806557	59807414	ENSMUSG00000091941	0.4674	+	Gm7399	processed_pseudogene	9.50e-06	1.46e-03	-2.82443	3.47e+00	2.18e-02	1.97e+00	2.39e-03	2.58e+00	6.25e+00	1.58e+00	3.99e-03	5.98e-02	1.59e-03	2.32e+00	1.58e+00	2.00e+00	3.19e-03	1.59e-03	2.39e-03
chr8	119700009	119757717	ENSMUSG00000034112	0.5157	+	Atp2c2	protein_coding	9.71e-06	1.47e-03	2.65896	2.52e+00	1.95e-03	5.21e+00	1.42e-02	3.17e+00	1.58e+00	2.81e+00	2.92e-03	7.30e-04	2.19e-03	5.83e+00	5.39e+00	4.39e+00	2.04e-02	1.50e-02	7.30e-03
chr7	51746647	51747878	ENSMUSG00000078636	0.5536	+	Gm7336	processed_pseudogene	1.01e-05	1.52e-03	2.36453	6.83e+00	9.68e-02	8.62e+00	5.02e-01	6.97e+00	6.07e+00	7.45e+00	9.89e-02	5.26e-02	1.39e-01	1.05e+01	8.03e+00	7.31e+00	1.17e+00	2.07e-01	1.26e-01
chr2	34849734	34874968	ENSMUSG00000026869	0.4509	-	Psmd5	protein_coding	1.03e-05	1.52e-03	-2.83180	8.66e+00	1.08e-01	6.64e+00	1.50e-02	7.83e+00	7.38e+00	1.08e+01	3.42e-02	2.51e-02	2.65e-01	6.36e+00	6.79e+00	6.75e+00	1.23e-02	1.67e-02	1.62e-02
chr1	143683230	143684298	ENSMUSG00000089640	0.4584	+	Gm6397	processed_pseudogene	1.11e-05	1.62e-03	-2.59991	3.86e+00	2.50e-02	2.41e+00	3.46e-03	2.58e+00	6.41e+00	2.58e+00	3.99e-03	6.70e-02	3.99e-03	2.58e+00	2.32e+00	2.32e+00	3.99e-03	3.19e-03	3.19e-03
chr1	9574548	9575649	ENSMUSG00000081441	0.4592	+	Gm6161	processed_pseudogene	1.16e-05	1.67e-03	-2.17742	6.24e+00	1.53e-01	5.15e+00	3.32e-02	4.64e+00	9.03e+00	5.04e+00	1.91e-02	4.15e-01	2.55e-02	6.21e+00	4.00e+00	5.25e+00	5.82e-02	1.20e-02	2.95e-02
chr15	77443285	77444420	ENSMUSG00000082233	0.4718	-	Gm6506	processed_pseudogene	1.21e-05	1.71e-03	-3.84130	1.79e+00	1.06e-02	0.00e+00	0.00e+00	0.00e+00	5.36e+00	0.00e+00	0.00e+00	3.19e-02	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr1	9547948	9580673	ENSMUSG00000025911	0.4316	+	Adhfe1	protein_coding	1.57e-05	2.18e-03	-2.13551	6.26e+00	1.19e-02	5.18e+00	2.65e-03	4.70e+00	9.03e+00	5.04e+00	1.54e-03	3.21e-02	1.97e-03	6.27e+00	4.00e+00	5.29e+00	4.69e-03	9.25e-04	2.34e-03
chr7	106156556	106215326	ENSMUSG00000045868	0.3966	-	Gvin1	protein_coding	1.58e-05	2.18e-03	-3.38247	4.59e+00	8.72e-03	7.74e-01	4.98e-04	4.75e+00	4.70e+00	4.32e+00	9.72e-03	9.35e-03	7.10e-03	0.00e+00	0.00e+00	2.32e+00	0.00e+00	0.00e+00	1.50e-03
chr8	57550299	57551303	ENSMUSG00000070025	0.5483	+	Gm10284	processed_pseudogene	1.88e-05	2.56e-03	2.25007	5.79e+00	4.63e-02	7.39e+00	2.23e-01	5.58e+00	5.32e+00	6.48e+00	3.75e-02	3.11e-02	7.02e-02	9.37e+00	6.95e+00	5.83e+00	5.26e-01	9.81e-02	4.47e-02
chr15	12574889	12575890	ENSMUSG00000078300	0.5509	+	Gm2606	processed_pseudogene	2.07e-05	2.79e-03	2.18830	5.56e+00	4.07e-02	7.09e+00	1.88e-01	5.25e+00	5.00e+00	6.43e+00	2.95e-02	2.47e-02	6.78e-02	9.13e+00	6.74e+00	5.39e+00	4.47e-01	8.45e-02	3.27e-02
chr10	80329953	80336441	ENSMUSG00000035504	0.5626	+	Reep6	protein_coding	2.39e-05	3.16e-03	2.63631	2.93e+00	1.74e-03	5.27e+00	1.22e-02	3.17e+00	2.81e+00	2.81e+00	2.09e-03	1.57e-03	1.57e-03	6.52e+00	4.91e+00	4.39e+00	2.37e-02	7.57e-03	5.22e-03
chr2	132253357	132261333	ENSMUSG00000087377	0.4139	+	AV099323	antisense	2.41e-05	3.16e-03	2.79256	1.77e+00	2.92e-03	3.94e+00	2.50e-02	3.00e+00	2.32e+00	0.00e+00	5.58e-03	3.19e-03	0.00e+00	6.36e+00	3.46e+00	2.00e+00	6.46e-02	7.97e-03	2.39e-03
chr18	88971790	89131013	ENSMUSG00000023066	0.3740	+	Rttn	protein_coding	2.52e-05	3.27e-03	2.51096	2.69e+00	3.58e-03	3.68e+00	2.34e-02	3.17e+00	2.32e+00	2.58e+00	5.05e-03	2.53e-03	3.16e-03	6.71e+00	2.32e+00	2.00e+00	6.57e-02	2.53e-03	1.90e-03
chr15	77729121	77736381	ENSMUSG00000068246	0.5092	+	Apol9b	protein_coding	2.56e-05	3.27e-03	-3.21723	4.93e+00	1.77e-02	1.27e+00	1.37e-03	5.25e+00	4.91e+00	4.64e+00	2.18e-02	1.71e-02	1.41e-02	2.81e+00	1.00e+00	0.00e+00	3.54e-03	5.89e-04	0.00e+00
chr17	87635979	87651106	ENSMUSG00000045394	0.4636	+	Epcam	protein_coding	2.59e-05	3.27e-03	2.56416	6.76e+00	5.69e-02	9.27e+00	3.39e-01	6.70e+00	6.86e+00	6.73e+00	5.45e-02	6.08e-02	5.55e-02	9.77e+00	8.81e+00	9.24e+00	4.61e-01	2.37e-01	3.19e-01
chr9	59539540	59565109	ENSMUSG00000025232	0.4706	+	Hexa	protein_coding	2.70e-05	3.37e-03	-3.93860	3.87e+00	9.69e-03	0.00e+00	0.00e+00	4.46e+00	3.70e+00	3.46e+00	1.42e-02	8.11e-03	6.76e-03	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr15	77403789	77411080	ENSMUSG00000057346	0.5078	-	Apol9a	protein_coding	3.18e-05	3.92e-03	-3.32193	4.22e+00	1.12e-02	8.62e-01	5.89e-04	4.46e+00	4.75e+00	3.46e+00	1.24e-02	1.53e-02	5.89e-03	1.58e+00	0.00e+00	1.00e+00	1.18e-03	0.00e+00	5.89e-04
chr1	130882074	130887454	ENSMUSG00000026420	0.4793	-	Il24	protein_coding	3.31e-05	4.05e-03	2.19421	5.51e+00	9.04e-03	7.45e+00	4.21e-02	5.67e+00	4.86e+00	6.00e+00	9.62e-03	5.39e-03	1.21e-02	8.58e+00	7.70e+00	6.07e+00	7.37e-02	3.98e-02	1.27e-02
chr11	97013394	97024734	ENSMUSG00000038560	0.5237	+	Sp6	protein_coding	3.49e-05	4.21e-03	3.07039	1.67e+00	6.87e-04	4.74e+00	7.95e-03	2.00e+00	2.00e+00	1.00e+00	8.84e-04	8.84e-04	2.95e-04	4.32e+00	5.32e+00	4.58e+00	5.60e-03	1.15e-02	6.77e-03
chr11	70647235	70651421	ENSMUSG00000040746	0.4968	+	Rnf167	protein_coding	3.67e-05	4.38e-03	2.55254	3.32e+00	9.59e-04	5.51e+00	6.15e-03	3.46e+00	3.32e+00	3.17e+00	1.07e-03	9.59e-04	8.53e-04	6.66e+00	5.88e+00	4.00e+00	1.07e-02	6.18e-03	1.60e-03
chr2	83812636	83883486	ENSMUSG00000048388	0.4113	+	Fam171b	protein_coding	3.85e-05	4.54e-03	-2.80735	5.30e+00	6.98e-03	2.39e+00	8.42e-04	5.43e+00	5.00e+00	5.46e+00	7.58e-03	5.59e-03	7.76e-03	1.58e+00	3.00e+00	2.58e+00	3.61e-04	1.26e-03	9.02e-04
chr11	120729489	120732114	ENSMUSG00000025150	0.5678	-	Cbr2	protein_coding	4.09e-05	4.77e-03	2.12315	5.59e+00	2.37e-02	7.86e+00	1.04e-01	5.13e+00	4.46e+00	7.18e+00	1.22e-02	7.51e-03	5.15e-02	8.84e+00	8.42e+00	6.32e+00	1.63e-01	1.22e-01	2.82e-02

Download the DEG result list for Tbil22_vs_Tdil22.
Download the whole result list for Tbil22_vs_Tdil22.

References

Anders, S., and Huber, W. (2010). Differential expression analysis for sequence count data. Genome Biol 11, R106.
Planet, E., Attolini, C.S., Reina, O., Flores, O., and Rossell, D. (2012). htSeqTools: high-throughput sequencing quality control, processing and visualization in R. Bioinformatics 28, 589-590.
Tarazona, S., Garcia-Alcalde, F., Dopazo, J., Ferrer, A., and Conesa, A. (2011). Differential expression in RNA-seq: a matter of depth. Genome Res 21, 2213-2223.
Risso, D., Schwartz, K., Sherlock, G., and Dudoit, S. (2011). GC-content normalization for RNA-Seq data. BMC Bioinformatics 12, 480.
Benjamini, Y., and Hochberg, Y. (1995). Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society Series B (Methodological) 57, 289-300.