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 (22551 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 (3639 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 0.07974 average reads per 100 bp), iii) genes with read counts below the median read counts of the total normalized count distribution (11831 genes with cutoff value 22 normalized read counts). The total number of genes excluded due to the application of gene filters was 8135. The total (unified) number of genes excluded due to the application of all filters was 35404. The resulting gene counts table was subjected to differential expression analysis for the contrasts wt versus tg using the Bioconductor package DESeq. The final numbers of differentially expressed genes were (per contrast): for the contrast wt versus tg, 1027 (135) statistically significant genes were found with a p-value (FDR or adjusted p-value) threshold of 0.05 and of these, 372 (67) were up-regulated, 277 (68) were down-regulated and 378 (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: wt, tg
Samples included: ED3R1_K471, ED3R2_K513, ED3R4_K518, ED3R8_K517, ED3R5_K473, ED3R6_K483
Samples excluded: none
Requested contrasts: wt_vs_tg
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, mean-difference plot, mean-variance plot, DEG biotype detection, volcano plot, DEG heatmap
Figure format: png, pdf
Output directory: /data/images/proton2/run423/wwwDouniLab/metaseqr_run423b
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: 05 minutes 35 seconds

Filtered genes

Number of filtered genes: 35404 which is the union of

Filtered because of zero reads: 22551
Filtered because of exon filters: 0
Filtered because of gene filters: 12853 which is the union of
- length: 3639 genes with filter cutoff value 500
- avg.reads: 4429 genes with filter cutoff value 0.07974482
- expression: 11831 genes further decomposed to (filter name, filtered genes, filter cutoff):
- - median: 11831 genes with filter cutoff value 22
- biotype: 66 genes with filter cutoff value rRNA, IG_V_pseudogene, TR_V_pseudogene

Differentially expressed genes

Number of differentially expressed genes per contrast:

wt_vs_tg: 1027 (135) statistically significant genes of which 372 (67) up regulated, 277 (68) down regulated and 378 (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

[ 2019-07-11 19:24:51 ] INFO 2019-07-11 19:24:51: Data processing started...
[ 2019-07-11 19:24:51 ] INFO Read counts file: imported custom data frame
[ 2019-07-11 19:24:51 ] INFO Conditions: wt, tg
[ 2019-07-11 19:24:51 ] INFO Samples to include: ED3R1_K471, ED3R2_K513, ED3R4_K518, ED3R8_K517, ED3R5_K473, ED3R6_K483
[ 2019-07-11 19:24:51 ] INFO Samples to exclude: none
[ 2019-07-11 19:24:51 ] INFO Requested contrasts: wt_vs_tg
[ 2019-07-11 19:24:51 ] INFO Annotation: embedded
[ 2019-07-11 19:24:51 ] INFO Organism: custom
[ 2019-07-11 19:24:51 ] INFO Reference source: ensembl
[ 2019-07-11 19:24:51 ] INFO Count type: utr
[ 2019-07-11 19:24:51 ] INFO Exon filters: none applied
[ 2019-07-11 19:24:51 ] INFO Gene filters: length, avg.reads, expression, biotype
[ 2019-07-11 19:24:51 ] INFO length:
[ 2019-07-11 19:24:51 ] INFO length: 500
[ 2019-07-11 19:24:51 ] INFO avg.reads:
[ 2019-07-11 19:24:51 ] INFO average.per.bp: 100
[ 2019-07-11 19:24:51 ] INFO quantile: 0.25
[ 2019-07-11 19:24:51 ] INFO expression:
[ 2019-07-11 19:24:51 ] INFO median: TRUE
[ 2019-07-11 19:24:51 ] INFO mean: FALSE
[ 2019-07-11 19:24:51 ] INFO quantile: NA
[ 2019-07-11 19:24:51 ] INFO known: NA
[ 2019-07-11 19:24:51 ] INFO custom: NA
[ 2019-07-11 19:24:51 ] INFO biotype:
[ 2019-07-11 19:24:51 ] INFO pseudogene: FALSE
[ 2019-07-11 19:24:51 ] INFO snRNA: FALSE
[ 2019-07-11 19:24:51 ] INFO protein_coding: FALSE
[ 2019-07-11 19:24:51 ] INFO antisense: FALSE
[ 2019-07-11 19:24:51 ] INFO miRNA: FALSE
[ 2019-07-11 19:24:51 ] INFO snoRNA: FALSE
[ 2019-07-11 19:24:51 ] INFO lincRNA: FALSE
[ 2019-07-11 19:24:51 ] INFO processed_transcript: FALSE
[ 2019-07-11 19:24:51 ] INFO misc_RNA: FALSE
[ 2019-07-11 19:24:51 ] INFO rRNA: TRUE
[ 2019-07-11 19:24:51 ] INFO sense_intronic: FALSE
[ 2019-07-11 19:24:51 ] INFO sense_overlapping: FALSE
[ 2019-07-11 19:24:51 ] INFO polymorphic_pseudogene: FALSE
[ 2019-07-11 19:24:51 ] INFO IG_C_gene: FALSE
[ 2019-07-11 19:24:51 ] INFO IG_J_gene: FALSE
[ 2019-07-11 19:24:51 ] INFO IG_D_gene: FALSE
[ 2019-07-11 19:24:51 ] INFO IG_LV_gene: FALSE
[ 2019-07-11 19:24:51 ] INFO IG_V_gene: FALSE
[ 2019-07-11 19:24:51 ] INFO IG_V_pseudogene: TRUE
[ 2019-07-11 19:24:51 ] INFO TR_V_gene: FALSE
[ 2019-07-11 19:24:51 ] INFO TR_V_pseudogene: TRUE
[ 2019-07-11 19:24:51 ] INFO three_prime_overlapping_ncrna: FALSE
[ 2019-07-11 19:24:51 ] INFO Filter application: postnorm
[ 2019-07-11 19:24:51 ] INFO Normalization algorithm: deseq
[ 2019-07-11 19:24:51 ] INFO Normalization arguments:
[ 2019-07-11 19:24:51 ] INFO locfunc:
[ 2019-07-11 19:24:51 ] INFO [[
[ 2019-07-11 19:24:51 ] INFO list(locfunc = function (x, na.rm = FALSE) standardGeneric("median"))
[ 2019-07-11 19:24:51 ] INFO locfunc
[ 2019-07-11 19:24:51 ] INFO Statistical algorithm: deseq
[ 2019-07-11 19:24:51 ] INFO Statistical arguments:
[ 2019-07-11 19:24:51 ] INFO deseq: blind, fit-only, local
[ 2019-07-11 19:24:51 ] INFO Meta-analysis method: none
[ 2019-07-11 19:24:51 ] INFO Multiple testing correction: BH
[ 2019-07-11 19:24:51 ] INFO p-value threshold: 0.05
[ 2019-07-11 19:24:51 ] INFO Logarithmic transformation offset: 1
[ 2019-07-11 19:24:51 ] INFO Quality control plots: mds, biodetection, countsbio, saturation, readnoise, filtered, correl, pairwise, boxplot, gcbias, lengthbias, meandiff, meanvar, biodist, volcano, deheatmap
[ 2019-07-11 19:24:51 ] INFO Figure format: png, pdf
[ 2019-07-11 19:24:51 ] INFO Output directory: /data/images/proton2/run423/wwwDouniLab/metaseqr_run423b
[ 2019-07-11 19:24:51 ] INFO Output data: annotation, p.value, adj.p.value, fold.change, stats, counts, flags
[ 2019-07-11 19:24:51 ] INFO Output scale(s): log2, rpgm
[ 2019-07-11 19:24:51 ] INFO Output values: normalized
[ 2019-07-11 19:24:51 ] INFO Output statistics: mean
[ 2019-07-11 19:24:51 ] INFO Reading custom external gene annotation for from /data/results/tools/rnaseq/metaseqr/mm10/gene_data_mm10.txt.gz...
[ 2019-07-11 19:24:53 ] INFO Checking chromosomes in transcript counts and gene annotation...
[ 2019-07-11 19:24:53 ] INFO Processing transcripts...
[ 2019-07-11 19:24:53 ] INFO Separating transcripts (UTR regions) per gene for ED3R1_K471...
[ 2019-07-11 19:25:18 ] INFO Separating transcripts (UTR regions) per gene for ED3R2_K513...
[ 2019-07-11 19:25:45 ] INFO Separating transcripts (UTR regions) per gene for ED3R4_K518...
[ 2019-07-11 19:26:13 ] INFO Separating transcripts (UTR regions) per gene for ED3R8_K517...
[ 2019-07-11 19:26:38 ] INFO Separating transcripts (UTR regions) per gene for ED3R5_K473...
[ 2019-07-11 19:27:01 ] INFO Separating transcripts (UTR regions) per gene for ED3R6_K483...
[ 2019-07-11 19:27:27 ] INFO Saving gene model to /data/images/proton2/run423/wwwDouniLab/metaseqr_run423b/data/gene_model.RData
[ 2019-07-11 19:27:29 ] INFO Summarizing count data...
[ 2019-07-11 19:27:33 ] INFO Removing genes with zero counts in all samples...
[ 2019-07-11 19:27:33 ] INFO Normalizing with: deseq
[ 2019-07-11 19:27:33 ] INFO Applying gene filter length...
[ 2019-07-11 19:27:33 ] INFO Threshold below which ignored: 500
[ 2019-07-11 19:27:33 ] INFO Applying gene filter avg.reads...
[ 2019-07-11 19:27:34 ] INFO Threshold below which ignored: 0.0797448165869219
[ 2019-07-11 19:27:34 ] INFO Applying gene filter expression...
[ 2019-07-11 19:27:34 ] INFO Threshold below which ignored: 22
[ 2019-07-11 19:27:34 ] INFO Applying gene filter biotype...
[ 2019-07-11 19:27:34 ] INFO Biotypes ignored: rRNA, IG_V_pseudogene, TR_V_pseudogene
[ 2019-07-11 19:27:34 ] INFO 35404 genes filtered out
[ 2019-07-11 19:27:34 ] INFO 14144 genes remain after filtering
[ 2019-07-11 19:27:34 ] INFO Running statistical tests with: deseq
[ 2019-07-11 19:27:35 ] INFO Contrast: wt_vs_tg
[ 2019-07-11 19:27:56 ] INFO Contrast wt_vs_tg: found 1027 genes
[ 2019-07-11 19:27:56 ] INFO Building output files...
[ 2019-07-11 19:27:59 ] INFO Contrast: wt_vs_tg
[ 2019-07-11 19:27:59 ] INFO Adding non-filtered data...
[ 2019-07-11 19:27:59 ] INFO binding annotation...
[ 2019-07-11 19:28:00 ] INFO binding p-values...
[ 2019-07-11 19:28:00 ] INFO binding FDRs...
[ 2019-07-11 19:28:00 ] INFO binding log2 normalized fold changes...
[ 2019-07-11 19:28:00 ] INFO binding normalized mean counts...
[ 2019-07-11 19:28:00 ] INFO binding normalized mean counts...
[ 2019-07-11 19:28:01 ] INFO binding all normalized counts for wt...
[ 2019-07-11 19:28:01 ] INFO binding all normalized counts for tg...
[ 2019-07-11 19:28:02 ] INFO binding filtering flags...
[ 2019-07-11 19:28:02 ] INFO Writing output...
[ 2019-07-11 19:28:02 ] INFO Adding filtered data...
[ 2019-07-11 19:28:02 ] INFO binding annotation...
[ 2019-07-11 19:28:02 ] INFO binding p-values...
[ 2019-07-11 19:28:02 ] INFO binding FDRs...
[ 2019-07-11 19:28:03 ] INFO binding log2 normalized fold changes...
[ 2019-07-11 19:28:03 ] INFO binding normalized mean counts...
[ 2019-07-11 19:28:04 ] INFO binding normalized mean counts...
[ 2019-07-11 19:28:05 ] INFO binding all normalized counts for wt...
[ 2019-07-11 19:28:05 ] INFO binding all normalized counts for tg...
[ 2019-07-11 19:28:05 ] INFO binding filtering flags...
[ 2019-07-11 19:28:05 ] INFO Writing output...
[ 2019-07-11 19:28:08 ] INFO Creating quality control graphs...
[ 2019-07-11 19:28:08 ] INFO Plotting in png format...
[ 2019-07-11 19:28:08 ] INFO Plotting mds...
[ 2019-07-11 19:28:08 ] INFO Plotting biodetection...
[ 2019-07-11 19:28:09 ] INFO Plotting countsbio...
[ 2019-07-11 19:28:10 ] INFO Plotting saturation...
[ 2019-07-11 19:28:17 ] INFO Plotting readnoise...
[ 2019-07-11 19:28:18 ] INFO Plotting correl...
[ 2019-07-11 19:28:18 ] INFO Plotting pairwise...
[ 2019-07-11 19:28:30 ] INFO Plotting boxplot...
[ 2019-07-11 19:28:30 ] INFO Plotting gcbias...
[ 2019-07-11 19:28:31 ] INFO Plotting lengthbias...
[ 2019-07-11 19:28:31 ] INFO Plotting meandiff...
[ 2019-07-11 19:28:32 ] INFO Plotting meanvar...
[ 2019-07-11 19:28:38 ] INFO Plotting boxplot...
[ 2019-07-11 19:28:38 ] INFO Plotting gcbias...
[ 2019-07-11 19:28:39 ] INFO Plotting lengthbias...
[ 2019-07-11 19:28:39 ] INFO Plotting meandiff...
[ 2019-07-11 19:28:40 ] INFO Plotting meanvar...
[ 2019-07-11 19:28:46 ] INFO Plotting biodist...
[ 2019-07-11 19:28:46 ] INFO Contrast: wt_vs_tg
[ 2019-07-11 19:28:47 ] INFO Plotting volcano...
[ 2019-07-11 19:28:47 ] INFO Contrast: wt_vs_tg
[ 2019-07-11 19:28:47 ] INFO Plotting deheatmap...
[ 2019-07-11 19:28:47 ] INFO Contrast: wt_vs_tg
[ 2019-07-11 19:29:20 ] INFO Plotting filtered...
[ 2019-07-11 19:29:20 ] INFO Plotting in pdf format...
[ 2019-07-11 19:29:20 ] INFO Plotting mds...
[ 2019-07-11 19:29:20 ] INFO Plotting biodetection...
[ 2019-07-11 19:29:20 ] INFO Plotting countsbio...
[ 2019-07-11 19:29:22 ] INFO Plotting saturation...
[ 2019-07-11 19:29:26 ] INFO Plotting readnoise...
[ 2019-07-11 19:29:27 ] INFO Plotting correl...
[ 2019-07-11 19:29:27 ] INFO Plotting pairwise...
[ 2019-07-11 19:29:33 ] INFO Plotting boxplot...
[ 2019-07-11 19:29:33 ] INFO Plotting gcbias...
[ 2019-07-11 19:29:34 ] INFO Plotting lengthbias...
[ 2019-07-11 19:29:35 ] INFO Plotting meandiff...
[ 2019-07-11 19:29:36 ] INFO Plotting meanvar...
[ 2019-07-11 19:29:44 ] INFO Plotting boxplot...
[ 2019-07-11 19:29:44 ] INFO Plotting gcbias...
[ 2019-07-11 19:29:45 ] INFO Plotting lengthbias...
[ 2019-07-11 19:29:45 ] INFO Plotting meandiff...
[ 2019-07-11 19:29:47 ] INFO Plotting meanvar...
[ 2019-07-11 19:29:53 ] INFO Plotting biodist...
[ 2019-07-11 19:29:53 ] INFO Contrast: wt_vs_tg
[ 2019-07-11 19:29:53 ] INFO Plotting volcano...
[ 2019-07-11 19:29:53 ] INFO Contrast: wt_vs_tg
[ 2019-07-11 19:29:54 ] INFO Plotting deheatmap...
[ 2019-07-11 19:29:54 ] INFO Contrast: wt_vs_tg
[ 2019-07-11 19:30:26 ] INFO Plotting filtered...
[ 2019-07-11 19:30:26 ] INFO Creating HTML report...
[ 2019-07-11 19:30:26 ] 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

Mean-difference

A mean-difference plot (or a Bland-Altman plot) 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. When the data cloud in a mean difference plot is centered around the horizontal zero line, this is an indication of good data quality and good normalization results. On the other hand, when the data cloud deviates from the center line or has a 'banana' shape, this constitutes an indication of systematic biases present in the data and that either the chosen normalization algorithm has not worked well, or that data are not normalized. The smoothing curve that traverses the data (red curve) summarizes the above trends.

Mean-difference plots for the replicates of wt

Mean-difference plots for the replicates of tg

Mean-variance

The mean-variance plot comprises a graphical means of displaying a possible relationship between the means of gene expression (counts) values and their variances across replicates of the same biological condition. Thus data can be inspected for possible overdispersion (greater variability in a dataset than would be expected based on a given simple statistical model). In such plots for RNA-Seq data, overdispersion is usually manifested as increasing variance with increasing gene expression (counts) and it is summarized through a smoothing curve (red curve). The following is taken from the EDASeq package vignette: '...although the Poisson distribution is a natural and simple way to model count data, it has the limitation of assuming equality of the mean and variance. For this reason, the negative binomial distribution has been proposed as an alternative when the data show over-dispersion...' If overdispersion is not present, the data cloud is expected to be evenly scattered around the smoothing curve.

Mean-variance plots for the replicates of wt

Mean-variance plots for the replicates of tg

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 wt_vs_tg

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 wt_vs_tg

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 wt_vs_tg

Get all the figures in png, pdf format.

Results

DEG table for the contrast wt_vs_tg

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 wt_vs_tg. 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_wt_vs_tg	log2_normalized_mean_counts_wt	rpgm_normalized_mean_counts_wt	log2_normalized_mean_counts_tg	rpgm_normalized_mean_counts_tg	log2_normalized_counts_ED3R1_K471	log2_normalized_counts_ED3R2_K513	log2_normalized_counts_ED3R4_K518	rpgm_normalized_counts_ED3R1_K471	rpgm_normalized_counts_ED3R2_K513	rpgm_normalized_counts_ED3R4_K518	log2_normalized_counts_ED3R8_K517	log2_normalized_counts_ED3R5_K473	log2_normalized_counts_ED3R6_K483	rpgm_normalized_counts_ED3R8_K517	rpgm_normalized_counts_ED3R5_K473	rpgm_normalized_counts_ED3R6_K483
chr5	73476447	73477353	ENSMUSG00000082039	0.5413	-	Gm15653	processed_pseudogene	1.67e-50	2.37e-46	-8.462161	4.68e+00	5.62e-01	1.00e+00	7.97e-04	2.00e+00	1.00e+00	1.10e+01	2.39e-03	7.97e-04	1.68e+00	1.00e+00	1.00e+00	1.00e+00	7.97e-04	7.97e-04	7.97e-04
chr5	87774567	87788797	ENSMUSG00000061937	0.3349	+	Csn1s2a	protein_coding	1.48e-47	1.05e-43	8.231564	2.68e+00	1.37e-03	7.01e+00	4.51e-01	4.46e+00	0.00e+00	3.58e+00	2.70e-03	0.00e+00	1.42e-03	1.34e+01	5.36e+00	2.32e+00	1.35e+00	5.15e-03	5.15e-04
chrX	90744546	90745062	ENSMUSG00000084289	0.4565	-	Gm6977	processed_pseudogene	1.36e-40	6.41e-37	-6.523562	6.16e+00	1.08e+00	3.69e+00	1.09e-02	2.58e+00	3.91e+00	1.20e+01	3.99e-03	1.12e-02	3.21e+00	2.58e+00	4.39e+00	4.09e+00	3.99e-03	1.59e-02	1.28e-02
chr5	87692624	87699630	ENSMUSG00000063157	0.3385	-	Csn2	protein_coding	8.05e-39	2.85e-35	8.035624	5.28e-01	1.22e-04	3.45e+00	7.98e-02	0.00e+00	0.00e+00	1.58e+00	0.00e+00	0.00e+00	3.66e-04	1.04e+01	0.00e+00	0.00e+00	2.39e-01	0.00e+00	0.00e+00
chr5	87666208	87682578	ENSMUSG00000070702	0.3159	+	Csn1s1	protein_coding	1.72e-34	4.85e-31	6.264258	3.11e+00	2.01e-03	6.18e+00	1.60e-01	3.00e+00	6.32e+00	0.00e+00	4.92e-04	5.55e-03	0.00e+00	1.27e+01	3.81e+00	2.00e+00	4.79e-01	9.13e-04	2.11e-04
chr15	98480400	98482683	ENSMUSG00000022991	0.4755	-	Lalba	protein_coding	2.07e-34	4.88e-31	7.100370	1.53e+00	1.30e-03	4.09e+00	2.66e-01	1.00e+00	1.58e+00	2.00e+00	6.48e-04	1.30e-03	1.94e-03	1.03e+01	0.00e+00	2.00e+00	7.97e-01	0.00e+00	1.94e-03
chr7	46740497	46742980	ENSMUSG00000074115	0.5382	-	Saa1	protein_coding	9.79e-31	1.98e-27	6.824959	1.19e+00	1.14e-03	3.80e+00	2.06e-01	2.00e+00	0.00e+00	1.58e+00	2.05e-03	0.00e+00	1.37e-03	9.82e+00	1.58e+00	0.00e+00	6.17e-01	1.37e-03	0.00e+00
chr3	95410346	95411207	ENSMUSG00000096177	0.5580	-	Gm5070	processed_pseudogene	2.88e-29	5.08e-26	-6.567685	4.31e+00	1.76e-01	1.00e+00	1.06e-03	1.00e+00	2.58e+00	9.36e+00	7.97e-04	3.99e-03	5.22e-01	0.00e+00	2.00e+00	1.00e+00	0.00e+00	2.39e-03	7.97e-04
chr5	104435118	104441050	ENSMUSG00000029304	0.3787	+	Spp1	protein_coding	1.26e-27	1.98e-24	6.202940	2.00e+00	7.09e-04	5.19e+00	6.94e-02	2.00e+00	2.00e+00	2.00e+00	7.09e-04	7.09e-04	7.09e-04	9.76e+00	3.00e+00	2.81e+00	2.05e-01	1.65e-03	1.42e-03
chr4	108450807	108451342	ENSMUSG00000083255	0.4646	-	Gm12738	processed_pseudogene	6.42e-25	9.08e-22	-7.026062	2.87e+00	1.03e-01	0.00e+00	0.00e+00	0.00e+00	0.00e+00	8.60e+00	0.00e+00	0.00e+00	3.09e-01	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chrX	164070705	164076493	ENSMUSG00000040749	0.4505	-	Siah1b	protein_coding	4.70e-24	6.04e-21	-4.926115	7.36e+00	2.73e-01	4.69e+00	8.67e-03	1.12e+01	6.54e+00	4.32e+00	7.83e-01	3.03e-02	6.26e-03	4.00e+00	4.81e+00	5.25e+00	4.94e-03	8.89e-03	1.22e-02
chr13	66874081	66874659	ENSMUSG00000095847	0.4819	+	Gm5451	processed_pseudogene	5.69e-23	6.71e-20	-4.051281	8.47e+00	7.53e-01	5.75e+00	4.47e-02	7.47e+00	6.61e+00	1.13e+01	1.40e-01	7.74e-02	2.04e+00	6.41e+00	5.36e+00	5.49e+00	6.70e-02	3.19e-02	3.51e-02
chr4	118961578	118968912	ENSMUSG00000024903	0.4916	+	Lao1	protein_coding	3.78e-22	4.11e-19	5.693487	1.64e+00	1.05e-03	4.14e+00	7.19e-02	2.58e+00	0.00e+00	2.32e+00	1.75e-03	0.00e+00	1.40e-03	9.26e+00	0.00e+00	3.17e+00	2.13e-01	0.00e+00	2.79e-03
chr13	23456992	23465901	ENSMUSG00000000706	0.4526	-	Btn1a1	protein_coding	4.79e-21	4.84e-18	4.516826	5.80e+00	1.60e-02	8.00e+00	3.72e-01	4.91e+00	5.21e+00	7.29e+00	6.31e-03	7.84e-03	3.40e-02	1.23e+01	5.55e+00	6.17e+00	1.09e+00	1.00e-02	1.55e-02
chr7	46751790	46754313	ENSMUSG00000057465	0.5452	+	Saa2	protein_coding	2.34e-18	2.21e-15	6.705056	0.00e+00	0.00e+00	3.09e+00	7.09e-02	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	8.28e+00	0.00e+00	1.00e+00	2.12e-01	0.00e+00	6.86e-04
chrX	103597771	103598336	ENSMUSG00000080796	0.4594	+	Gm9159	processed_pseudogene	1.42e-16	1.26e-13	-6.228819	3.26e+00	5.90e-02	0.00e+00	0.00e+00	2.00e+00	0.00e+00	7.78e+00	2.39e-03	0.00e+00	1.75e-01	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr10	19621998	19634681	ENSMUSG00000039760	0.4032	+	Il22ra2	protein_coding	1.11e-13	9.21e-11	-3.724027	7.53e+00	6.10e-02	3.76e+00	4.31e-03	7.55e+00	7.35e+00	7.67e+00	6.20e-02	5.37e-02	6.73e-02	3.58e+00	4.25e+00	3.46e+00	3.65e-03	5.97e-03	3.31e-03
chr2	70458982	70459560	ENSMUSG00000047965	0.4870	+	Rpl9-ps7	processed_pseudogene	1.48e-13	1.16e-10	-2.879429	7.04e+00	3.86e-01	6.00e+00	5.18e-02	5.64e+00	5.04e+00	1.04e+01	3.91e-02	2.55e-02	1.09e+00	6.15e+00	5.52e+00	6.34e+00	5.58e-02	3.59e-02	6.38e-02
chr8	21776599	21795185	ENSMUSG00000044748	0.4015	+	Defb1	protein_coding	2.69e-13	2.00e-10	-3.480432	7.56e+00	1.23e-01	3.18e+00	1.06e-02	8.73e+00	7.47e+00	6.49e+00	2.28e-01	9.45e-02	4.78e-02	1.00e+00	5.73e+00	2.81e+00	5.37e-04	2.79e-02	3.22e-03
chr7	121865038	121918514	ENSMUSG00000030873	0.4878	+	Scnn1b	protein_coding	1.46e-12	1.03e-09	3.132091	5.77e+00	1.28e-02	7.77e+00	1.14e-01	5.49e+00	5.32e+00	6.51e+00	9.73e-03	8.63e-03	1.99e-02	1.04e+01	6.73e+00	6.17e+00	3.02e-01	2.32e-02	1.57e-02
chr7	16514400	16515138	ENSMUSG00000101135	0.4899	-	Gm17981	processed_pseudogene	2.85e-12	1.92e-09	-5.727920	2.43e+00	4.15e-02	0.00e+00	0.00e+00	0.00e+00	0.00e+00	7.29e+00	0.00e+00	0.00e+00	1.24e-01	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr9	108358202	108359069	ENSMUSG00000104280	0.5703	-	Rps2-ps11	processed_pseudogene	4.03e-12	2.50e-09	-4.282554	3.32e+00	6.65e-02	1.67e+00	2.66e-03	1.00e+00	1.00e+00	7.96e+00	7.97e-04	7.97e-04	1.98e-01	0.00e+00	3.00e+00	2.00e+00	0.00e+00	5.58e-03	2.39e-03
chr6	79048116	79048824	ENSMUSG00000107772	0.4993	+	Gm5311	processed_pseudogene	4.07e-12	2.50e-09	-5.185867	2.83e+00	4.76e-02	6.67e-01	5.32e-04	1.00e+00	0.00e+00	7.48e+00	7.97e-04	0.00e+00	1.42e-01	1.00e+00	1.00e+00	0.00e+00	7.97e-04	7.97e-04	0.00e+00
chr5	87925579	87932665	ENSMUSG00000001622	0.3415	+	Csn3	protein_coding	4.89e-12	2.88e-09	3.005051	7.00e+00	2.75e-02	7.49e+00	2.22e-01	6.32e+00	8.42e+00	6.25e+00	1.32e-02	5.69e-02	1.25e-02	1.19e+01	5.52e+00	5.00e+00	6.54e-01	7.49e-03	5.16e-03
chr5	31193227	31199143	ENSMUSG00000029146	0.5080	+	Snx17	protein_coding	1.40e-11	7.94e-09	-2.607571	1.01e+01	2.38e-01	8.81e+00	3.90e-02	8.71e+00	8.81e+00	1.29e+01	3.47e-02	3.70e-02	6.42e-01	9.34e+00	8.85e+00	8.26e+00	5.35e-02	3.81e-02	2.53e-02
chr1	37026596	37187613	ENSMUSG00000050122	0.4499	+	Vwa3b	protein_coding	3.96e-11	2.07e-08	-4.371559	3.93e+00	3.28e-03	1.27e+00	1.13e-04	2.58e+00	1.58e+00	7.63e+00	2.41e-04	9.65e-05	9.50e-03	2.81e+00	0.00e+00	1.00e+00	2.89e-04	0.00e+00	4.82e-05
chr15	77015489	77050670	ENSMUSG00000018893	0.4993	-	Mb	protein_coding	3.96e-11	2.07e-08	-3.191951	5.19e+00	1.16e-01	4.38e+00	1.22e-02	2.58e+00	9.16e+00	3.81e+00	2.96e-03	3.39e-01	7.69e-03	4.95e+00	4.09e+00	4.09e+00	1.77e-02	9.46e-03	9.46e-03
chr4	139082284	139092993	ENSMUSG00000041120	0.5526	-	Nbl1	protein_coding	6.41e-11	3.24e-08	2.874392	8.14e+00	7.94e-02	9.52e+00	5.84e-01	8.22e+00	7.95e+00	8.26e+00	8.35e-02	6.89e-02	8.57e-02	1.25e+01	7.52e+00	8.55e+00	1.60e+00	5.13e-02	1.05e-01
chr11	67171027	67197517	ENSMUSG00000033196	0.4263	+	Myh2	protein_coding	1.42e-10	6.95e-08	-4.154818	3.57e+00	3.56e-02	2.11e+00	1.64e-03	0.00e+00	8.12e+00	2.58e+00	0.00e+00	1.05e-01	1.90e-03	2.00e+00	1.00e+00	3.32e+00	1.14e-03	3.79e-04	3.41e-03
chr3	87357881	87371073	ENSMUSG00000015854	0.4301	+	Cd5l	protein_coding	1.96e-10	9.22e-08	2.564942	6.97e+00	6.07e-02	9.53e+00	3.61e-01	7.21e+00	6.44e+00	7.25e+00	6.97e-02	4.08e-02	7.16e-02	9.99e+00	9.14e+00	9.47e+00	4.81e-01	2.68e-01	3.36e-01
chr7	19404776	19422841	ENSMUSG00000030399	0.5189	+	Ckm	protein_coding	2.83e-10	1.29e-07	2.781619	6.59e+00	3.37e-02	7.75e+00	2.33e-01	4.70e+00	7.23e+00	7.83e+00	6.31e-03	3.76e-02	5.73e-02	2.81e+00	1.10e+01	9.40e+00	1.51e-03	5.28e-01	1.71e-01
chr1	182127406	182128189	ENSMUSG00000103018	0.5421	-	Gm17968	processed_pseudogene	5.50e-10	2.43e-07	-5.437405	2.99e+00	3.38e-02	0.00e+00	0.00e+00	2.00e+00	0.00e+00	6.97e+00	2.39e-03	0.00e+00	9.89e-02	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr15	37331989	37332666	ENSMUSG00000089811	0.4912	-	Gm16138	processed_pseudogene	1.26e-09	5.38e-07	-5.087463	2.69e+00	3.54e-02	3.33e-01	2.66e-04	0.00e+00	1.00e+00	7.06e+00	0.00e+00	7.97e-04	1.05e-01	0.00e+00	0.00e+00	1.00e+00	0.00e+00	0.00e+00	7.97e-04
chr12	113254830	113260236	ENSMUSG00000095079	0.5130	-	Igha	IG_C_gene	1.43e-09	5.87e-07	2.591251	8.65e+00	1.06e-01	1.00e+01	6.41e-01	9.25e+00	8.41e+00	8.28e+00	1.54e-01	8.59e-02	7.86e-02	1.27e+01	8.29e+00	8.99e+00	1.71e+00	7.93e-02	1.29e-01
chr1	171518122	171535294	ENSMUSG00000038209	0.4250	-	Itln1	protein_coding	1.45e-09	5.87e-07	-5.470320	2.34e+00	1.65e-02	0.00e+00	0.00e+00	0.00e+00	7.03e+00	0.00e+00	0.00e+00	4.95e-02	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr7	100006172	100034728	ENSMUSG00000030732	0.5094	+	Chrdl2	protein_coding	2.62e-09	1.03e-06	5.554589	0.00e+00	0.00e+00	2.70e+00	1.78e-02	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	7.11e+00	1.00e+00	0.00e+00	5.30e-02	3.87e-04	0.00e+00
chr19	4861223	4877909	ENSMUSG00000006457	0.5070	-	Actn3	protein_coding	5.34e-09	2.04e-06	2.789944	3.98e+00	3.10e-02	6.07e+00	2.19e-01	0.00e+00	5.58e+00	6.36e+00	0.00e+00	3.42e-02	5.89e-02	1.00e+00	9.41e+00	7.81e+00	7.27e-04	4.93e-01	1.62e-01
chr3	89229057	89233381	ENSMUSG00000042784	0.5357	+	Muc1	protein_coding	7.23e-09	2.69e-06	2.581499	5.49e+00	1.62e-02	6.92e+00	9.90e-02	5.83e+00	5.64e+00	5.00e+00	2.01e-02	1.76e-02	1.11e-02	9.52e+00	5.64e+00	5.58e+00	2.63e-01	1.76e-02	1.68e-02
chr13	41182495	41220405	ENSMUSG00000021364	0.4502	-	Elovl2	protein_coding	1.13e-08	4.09e-06	-4.348728	2.77e+00	1.32e-02	8.62e-01	4.11e-04	0.00e+00	1.00e+00	7.32e+00	0.00e+00	2.47e-04	3.92e-02	0.00e+00	0.00e+00	2.58e+00	0.00e+00	0.00e+00	1.23e-03
chr11	102475915	102556392	ENSMUSG00000034621	0.4147	-	Gpatch8	protein_coding	1.99e-08	7.05e-06	2.428898	7.92e+00	2.92e-02	9.28e+00	1.58e-01	7.97e+00	7.47e+00	8.34e+00	2.91e-02	2.06e-02	3.78e-02	1.18e+01	7.95e+00	8.11e+00	4.12e-01	2.88e-02	3.23e-02
chr11	100131758	100135928	ENSMUSG00000054146	0.5251	-	Krt15	protein_coding	2.41e-08	8.31e-06	-2.546051	7.54e+00	8.34e-02	4.85e+00	1.40e-02	7.48e+00	8.76e+00	6.38e+00	6.40e-02	1.56e-01	2.96e-02	3.00e+00	6.04e+00	5.49e+00	2.53e-03	2.35e-02	1.59e-02
chr18	58008623	58209926	ENSMUSG00000024598	0.4214	-	Fbn2	protein_coding	2.71e-08	9.13e-06	-2.487572	7.65e+00	7.73e-02	5.30e+00	1.35e-02	7.10e+00	8.57e+00	7.29e+00	4.71e-02	1.31e-01	5.37e-02	5.64e+00	5.09e+00	5.17e+00	1.70e-02	1.14e-02	1.21e-02
chr6	146782469	146783274	ENSMUSG00000107788	0.5633	+	Gm6266	processed_pseudogene	3.61e-08	1.19e-05	-3.906891	3.81e+00	4.31e-02	1.53e+00	2.13e-03	3.17e+00	1.00e+00	7.27e+00	6.38e-03	7.97e-04	1.22e-01	0.00e+00	2.58e+00	2.00e+00	0.00e+00	3.99e-03	2.39e-03
chr18	43728069	43737237	ENSMUSG00000024503	0.3967	-	Spink1	protein_coding	4.60e-08	1.47e-05	-3.707819	4.97e+00	4.78e-02	2.11e+00	2.97e-03	4.75e+00	7.34e+00	2.81e+00	1.93e-02	1.20e-01	4.46e-03	2.32e+00	3.00e+00	1.00e+00	2.97e-03	5.20e-03	7.43e-04
chr7	114631478	114636357	ENSMUSG00000030669	0.4656	-	Calca	protein_coding	4.66e-08	1.47e-05	-3.192645	6.30e+00	2.16e-02	3.18e+00	2.21e-03	7.16e+00	7.82e+00	3.91e+00	2.42e-02	3.83e-02	2.38e-03	1.58e+00	3.00e+00	4.95e+00	3.40e-04	1.19e-03	5.11e-03
chr16	92485736	92541243	ENSMUSG00000022949	0.4702	+	Clic6	protein_coding	5.61e-08	1.73e-05	2.885357	4.08e+00	4.09e-03	5.74e+00	3.18e-02	4.58e+00	3.32e+00	4.32e+00	5.54e-03	2.17e-03	4.57e-03	8.48e+00	4.64e+00	4.09e+00	8.57e-02	5.78e-03	3.85e-03
chr12	113271174	113273248	ENSMUSG00000087642	0.4790	-	Ighe	IG_C_gene	8.43e-08	2.54e-05	-5.115477	2.98e+00	2.49e-02	0.00e+00	0.00e+00	2.32e+00	0.00e+00	6.61e+00	2.96e-03	0.00e+00	7.17e-02	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr6	48872896	48909188	ENSMUSG00000029811	0.4446	+	Aoc1	protein_coding	1.14e-07	3.36e-05	2.570396	5.83e+00	3.96e-02	4.75e+00	2.37e-01	4.00e+00	5.21e+00	8.29e+00	4.93e-03	1.18e-02	1.02e-01	0.00e+00	1.11e+01	3.17e+00	0.00e+00	7.08e-01	2.63e-03
chr1	134182176	134190181	ENSMUSG00000064246	0.5034	+	Chil1	protein_coding	2.55e-07	7.36e-05	2.832890	3.86e+00	4.46e-03	5.95e+00	3.36e-02	3.70e+00	4.70e+00	3.17e+00	3.57e-03	7.43e-03	2.38e-03	8.15e+00	4.64e+00	5.04e+00	8.41e-02	7.13e-03	9.51e-03
chr11	34204338	34208089	ENSMUSG00000047861	0.5136	-	Foxi1	protein_coding	2.95e-07	8.34e-05	3.371256	2.27e+00	2.75e-03	5.47e+00	3.23e-02	3.00e+00	0.00e+00	3.81e+00	2.89e-03	0.00e+00	5.36e-03	7.59e+00	3.91e+00	4.91e+00	7.91e-02	5.77e-03	1.20e-02
chr17	26542760	26543715	ENSMUSG00000068604	0.5722	+	Gm8225	protein_coding	3.45e-07	9.56e-05	-4.820179	2.92e+00	2.92e-02	3.33e-01	2.66e-04	2.00e+00	0.00e+00	6.75e+00	2.39e-03	0.00e+00	8.53e-02	0.00e+00	0.00e+00	1.00e+00	0.00e+00	0.00e+00	7.97e-04
chr12	113285325	113288932	ENSMUSG00000076612	0.5125	-	Ighg2c	IG_C_gene	3.57e-07	9.70e-05	-2.344095	6.73e+00	1.00e-01	5.24e+00	1.94e-02	4.46e+00	9.42e+00	6.32e+00	8.04e-03	2.62e-01	3.02e-02	3.58e+00	5.52e+00	6.60e+00	4.21e-03	1.72e-02	3.68e-02
chr9	53850164	53854560	ENSMUSG00000042045	0.4223	+	Sln	protein_coding	4.19e-07	1.12e-04	-4.807355	2.26e+00	1.62e-02	3.33e-01	1.48e-04	0.00e+00	6.78e+00	0.00e+00	0.00e+00	4.85e-02	0.00e+00	0.00e+00	0.00e+00	1.00e+00	0.00e+00	0.00e+00	4.45e-04
chr9	121489824	121495694	ENSMUSG00000032532	0.4916	-	Cck	protein_coding	9.74e-07	2.55e-04	3.731804	1.19e+00	1.30e-03	3.69e+00	2.17e-02	0.00e+00	0.00e+00	3.58e+00	0.00e+00	0.00e+00	3.91e-03	7.48e+00	1.58e+00	2.00e+00	6.33e-02	7.12e-04	1.07e-03
chrX	157698910	157752591	ENSMUSG00000041476	0.4014	+	Smpx	protein_coding	1.34e-06	3.44e-04	2.257042	5.13e+00	1.02e-02	7.03e+00	4.96e-02	2.32e+00	6.51e+00	6.57e+00	6.54e-04	1.47e-02	1.54e-02	3.91e+00	9.40e+00	7.79e+00	2.29e-03	1.10e-01	3.60e-02
chr2	110418170	110419619	ENSMUSG00000083320	0.4497	-	Gm13935	processed_pseudogene	1.72e-06	4.35e-04	-2.960254	3.80e+00	4.68e-02	2.92e+00	5.32e-03	3.00e+00	1.00e+00	7.40e+00	5.58e-03	7.97e-04	1.34e-01	2.58e+00	3.00e+00	3.17e+00	3.99e-03	5.58e-03	6.38e-03
chr2	153875045	153895270	ENSMUSG00000027481	0.5016	+	Bpifb2	protein_coding	2.23e-06	5.53e-04	2.946670	3.19e+00	3.40e-03	6.23e+00	2.86e-02	3.17e+00	4.09e+00	2.32e+00	2.91e-03	5.82e-03	1.46e-03	5.86e+00	6.92e+00	5.91e+00	2.07e-02	4.37e-02	2.15e-02
chr4	82897920	83052339	ENSMUSG00000059049	0.4222	-	Frem1	protein_coding	2.51e-06	6.12e-04	-2.391321	6.59e+00	1.06e-02	4.33e+00	1.96e-03	7.25e+00	7.47e+00	5.04e+00	1.34e-02	1.57e-02	2.85e-03	4.91e+00	4.91e+00	3.17e+00	2.58e-03	2.58e-03	7.12e-04
chr2	32384633	32388252	ENSMUSG00000026822	0.5470	-	Lcn2	protein_coding	3.77e-06	9.03e-04	1.884152	7.56e+00	3.58e-02	8.77e+00	1.33e-01	7.51e+00	7.52e+00	7.65e+00	3.45e-02	3.49e-02	3.81e-02	1.07e+01	7.64e+00	8.01e+00	3.12e-01	3.77e-02	4.90e-02
chr2	164777161	164779967	ENSMUSG00000017300	0.5426	-	Tnnc2	protein_coding	4.57e-06	1.08e-03	2.156234	1.01e+01	7.32e-01	1.13e+01	3.27e+00	7.32e+00	1.15e+01	1.14e+01	6.00e-02	1.11e+00	1.03e+00	7.01e+00	1.43e+01	1.26e+01	4.83e-02	7.47e+00	2.28e+00
chr15	78191114	78206400	ENSMUSG00000005716	0.5225	-	Pvalb	protein_coding	5.93e-06	1.37e-03	2.084802	8.50e+00	2.41e-01	9.09e+00	1.02e+00	7.12e+00	9.16e+00	9.22e+00	7.64e-02	3.17e-01	3.30e-01	5.09e+00	1.21e+01	1.01e+01	1.83e-02	2.46e+00	5.98e-01
chr2	143150830	143151630	ENSMUSG00000083047	0.4906	-	Gm14082	processed_pseudogene	8.16e-06	1.85e-03	-4.772590	2.94e+00	2.10e-02	0.00e+00	0.00e+00	2.58e+00	0.00e+00	6.23e+00	3.99e-03	0.00e+00	5.90e-02	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr5	92346638	92348889	ENSMUSG00000034855	0.4720	-	Cxcl10	protein_coding	8.26e-06	1.85e-03	1.942622	5.13e+00	1.35e-02	6.62e+00	5.30e-02	5.36e+00	5.46e+00	4.58e+00	1.53e-02	1.64e-02	8.79e-03	6.78e+00	4.95e+00	8.12e+00	4.17e-02	1.15e-02	1.06e-01
chr11	43670801	43671484	ENSMUSG00000083662	0.5044	+	Gm12152	processed_pseudogene	8.88e-06	1.96e-03	-4.459432	2.14e+00	2.26e-02	3.33e-01	2.66e-04	0.00e+00	0.00e+00	6.43e+00	0.00e+00	0.00e+00	6.78e-02	1.00e+00	0.00e+00	0.00e+00	7.97e-04	0.00e+00	0.00e+00
chr9	45906497	45929726	ENSMUSG00000035382	0.4761	+	Pcsk7	protein_coding	9.21e-06	2.00e-03	2.102021	5.26e+00	2.70e-03	7.36e+00	1.18e-02	4.95e+00	5.39e+00	5.43e+00	2.15e-03	2.94e-03	3.01e-03	7.64e+00	7.10e+00	7.34e+00	1.42e-02	9.74e-03	1.15e-02
chr2	103411688	103450989	ENSMUSG00000027186	0.4646	+	Elf5	protein_coding	1.06e-05	2.28e-03	1.873501	6.39e+00	1.75e-02	7.22e+00	6.46e-02	6.23e+00	7.12e+00	5.83e+00	1.45e-02	2.70e-02	1.10e-02	9.76e+00	5.73e+00	6.17e+00	1.70e-01	1.02e-02	1.39e-02
chr13	71957921	71963723	ENSMUSG00000060969	0.5346	-	Irx1	protein_coding	1.09e-05	2.30e-03	1.701692	7.14e+00	7.53e-02	8.57e+00	2.46e-01	7.47e+00	7.46e+00	6.49e+00	9.03e-02	8.98e-02	4.57e-02	9.88e+00	7.53e+00	8.30e+00	4.82e-01	9.44e-02	1.62e-01
chr7	28863853	28866284	ENSMUSG00000053522	0.5493	+	Lgals7	protein_coding	1.30e-05	2.71e-03	-1.857803	1.00e+01	3.50e-01	8.47e+00	9.63e-02	1.07e+01	1.10e+01	8.46e+00	4.33e-01	5.25e-01	9.11e-02	9.09e+00	8.15e+00	8.16e+00	1.41e-01	7.37e-02	7.40e-02
chr8	123891769	123894751	ENSMUSG00000031972	0.5675	-	Acta1	protein_coding	1.47e-05	3.01e-03	1.899737	9.03e+00	2.53e-01	9.57e+00	9.45e-01	7.35e+00	1.02e+01	9.50e+00	5.90e-02	4.37e-01	2.63e-01	5.00e+00	1.23e+01	1.14e+01	1.13e-02	1.84e+00	9.89e-01
chr1	73522095	73529790	ENSMUSG00000100444	0.4141	-	C530043A13Rik	lincRNA	1.52e-05	3.06e-03	3.930737	8.62e-01	6.64e-04	3.06e+00	1.58e-02	0.00e+00	2.58e+00	0.00e+00	0.00e+00	1.99e-03	0.00e+00	6.86e+00	0.00e+00	2.32e+00	4.58e-02	0.00e+00	1.59e-03
chr1	143093347	143094616	ENSMUSG00000101795	0.4898	-	Gm5835	processed_pseudogene	1.63e-05	3.20e-03	-1.774051	1.09e+01	1.55e+00	9.12e+00	4.53e-01	1.08e+01	1.14e+01	1.04e+01	1.44e+00	2.16e+00	1.06e+00	9.55e+00	9.03e+00	8.78e+00	5.96e-01	4.15e-01	3.49e-01
chr5	53038081	53071664	ENSMUSG00000029188	0.4675	+	Slc34a2	protein_coding	1.63e-05	3.20e-03	3.278859	1.93e+00	8.05e-04	4.24e+00	9.32e-03	2.32e+00	0.00e+00	3.46e+00	6.90e-04	0.00e+00	1.73e-03	7.24e+00	2.32e+00	3.17e+00	2.59e-02	6.90e-04	1.38e-03
chr7	142441808	142444410	ENSMUSG00000031097	0.5659	+	Tnni2	protein_coding	1.90e-05	3.69e-03	1.995414	8.96e+00	2.45e-01	9.55e+00	9.77e-01	7.09e+00	9.93e+00	9.87e+00	4.85e-02	3.51e-01	3.35e-01	5.25e+00	1.26e+01	1.07e+01	1.33e-02	2.30e+00	6.15e-01
chr7	127208890	127214298	ENSMUSG00000030672	0.5010	+	Mylpf	protein_coding	2.21e-05	4.23e-03	1.882159	8.47e+00	2.01e-01	9.19e+00	7.41e-01	6.43e+00	1.00e+01	8.98e+00	3.17e-02	3.83e-01	1.88e-01	4.81e+00	1.21e+01	1.07e+01	1.01e-02	1.58e+00	6.30e-01
chr5	122643911	122691432	ENSMUSG00000029468	0.4751	+	P2rx7	protein_coding	2.73e-05	5.16e-03	1.708739	6.51e+00	1.27e-02	8.40e+00	4.17e-02	6.69e+00	5.39e+00	7.45e+00	1.22e-02	4.92e-03	2.09e-02	8.22e+00	8.09e+00	8.89e+00	3.58e-02	3.26e-02	5.67e-02
chr6	144993835	145076184	ENSMUSG00000030268	0.4628	-	Bcat1	protein_coding	2.84e-05	5.29e-03	-1.832123	7.26e+00	1.08e-02	5.28e+00	2.97e-03	6.88e+00	7.23e+00	7.66e+00	8.09e-03	1.03e-02	1.39e-02	4.39e+00	6.19e+00	5.25e+00	1.38e-03	4.98e-03	2.56e-03
chr9	110741861	110769798	ENSMUSG00000059741	0.5316	+	Myl3	protein_coding	4.44e-05	8.10e-03	-2.584963	3.95e+00	1.53e-02	3.15e+00	2.32e-03	0.00e+00	7.21e+00	4.64e+00	0.00e+00	3.94e-02	6.43e-03	3.32e+00	3.81e+00	2.32e+00	2.41e-03	3.49e-03	1.07e-03
chr13	93040713	93144724	ENSMUSG00000047419	0.4315	-	Cmya5	protein_coding	4.47e-05	8.10e-03	2.286739	4.21e+00	1.44e-02	6.38e+00	7.22e-02	2.32e+00	4.52e+00	5.78e+00	2.16e-03	1.19e-02	2.91e-02	4.70e+00	8.26e+00	6.19e+00	1.35e-02	1.64e-01	3.88e-02
chr9	58063788	58153996	ENSMUSG00000032327	0.5069	+	Stra6	protein_coding	4.55e-05	8.14e-03	-2.790077	5.61e+00	7.38e-03	2.90e+00	9.51e-04	5.95e+00	6.34e+00	4.52e+00	8.28e-03	1.09e-02	2.99e-03	2.32e+00	3.58e+00	2.81e+00	5.43e-04	1.49e-03	8.15e-04
chr3	19957054	20009145	ENSMUSG00000003617	0.3946	+	Cp	protein_coding	4.80e-05	8.42e-03	1.718590	9.10e+00	2.64e-02	1.02e+01	8.71e-02	9.23e+00	9.39e+00	8.68e+00	2.84e-02	3.16e-02	1.93e-02	1.20e+01	9.64e+00	9.01e+00	1.99e-01	3.77e-02	2.43e-02
chr6	83742990	83758855	ENSMUSG00000006269	0.5292	+	Atp6v1b1	protein_coding	4.82e-05	8.42e-03	1.711266	7.76e+00	2.80e-02	8.86e+00	9.19e-02	7.22e+00	7.89e+00	8.16e+00	1.85e-02	2.96e-02	3.58e-02	1.08e+01	7.79e+00	8.03e+00	2.16e-01	2.75e-02	3.25e-02
chr13	30336441	30382867	ENSMUSG00000049115	0.4305	+	Agtr1a	protein_coding	5.08e-05	8.75e-03	-1.673772	8.94e+00	1.80e-01	7.55e+00	5.63e-02	9.58e+00	9.76e+00	7.48e+00	2.29e-01	2.59e-01	5.33e-02	7.38e+00	7.76e+00	7.52e+00	4.97e-02	6.46e-02	5.45e-02
chr1	120474538	120505024	ENSMUSG00000026390	0.4694	-	Marco	protein_coding	5.18e-05	8.82e-03	1.694283	9.68e+00	3.18e-01	1.14e+01	1.03e+00	9.68e+00	9.35e+00	1.00e+01	3.11e-01	2.48e-01	3.95e-01	1.08e+01	1.17e+01	1.16e+01	6.89e-01	1.26e+00	1.14e+00
chr1	107361198	107380475	ENSMUSG00000026327	0.4055	+	Serpinb11	protein_coding	5.53e-05	9.31e-03	-4.058894	3.61e+00	1.09e-02	6.67e-01	3.36e-04	2.32e+00	6.51e+00	2.00e+00	1.34e-03	3.02e-02	1.01e-03	0.00e+00	0.00e+00	2.00e+00	0.00e+00	0.00e+00	1.01e-03
chr11	100256217	100261029	ENSMUSG00000035557	0.5439	-	Krt17	protein_coding	5.89e-05	9.80e-03	-1.867362	6.88e+00	9.31e-02	4.78e+00	2.50e-02	7.62e+00	7.28e+00	5.73e+00	1.36e-01	1.07e-01	3.61e-02	4.64e+00	6.23e+00	3.46e+00	1.67e-02	5.14e-02	6.95e-03
chr11	5955693	5967780	ENSMUSG00000002741	0.4793	+	Ykt6	protein_coding	6.51e-05	1.07e-02	-1.938114	1.02e+01	5.45e-01	9.22e+00	1.42e-01	1.25e+01	8.90e+00	9.25e+00	1.38e+00	1.12e-01	1.43e-01	8.96e+00	9.19e+00	9.51e+00	1.17e-01	1.37e-01	1.71e-01
chr2	164497520	164508301	ENSMUSG00000017721	0.5088	+	Pigt	protein_coding	8.18e-05	1.33e-02	-1.713182	1.16e+01	2.19e-01	1.00e+01	6.68e-02	1.24e+01	1.20e+01	1.04e+01	3.32e-01	2.46e-01	7.95e-02	1.06e+01	9.99e+00	9.48e+00	9.60e-02	6.13e-02	4.31e-02
chr10	79910053	79917006	ENSMUSG00000035781	0.5615	-	R3hdm4	protein_coding	8.83e-05	1.42e-02	-1.761356	9.57e+00	4.82e-01	8.29e+00	1.42e-01	1.12e+01	9.04e+00	8.47e+00	1.05e+00	2.35e-01	1.58e-01	7.95e+00	8.59e+00	8.33e+00	1.10e-01	1.72e-01	1.43e-01
chr8	3630955	3643644	ENSMUSG00000004626	0.5132	+	Stxbp2	protein_coding	9.52e-05	1.51e-02	1.785102	9.03e+00	3.90e-02	9.87e+00	1.35e-01	9.02e+00	9.03e+00	9.04e+00	3.86e-02	3.91e-02	3.92e-02	8.59e+00	1.22e+01	8.85e+00	2.87e-02	3.40e-01	3.45e-02
chr7	110800432	110844457	ENSMUSG00000030788	0.4434	-	Rnf141	protein_coding	1.07e-04	1.67e-02	1.481598	9.73e+00	6.49e-02	1.09e+01	1.81e-01	9.85e+00	9.48e+00	9.85e+00	7.04e-02	5.43e-02	7.00e-02	9.78e+00	1.10e+01	1.20e+01	6.70e-02	1.56e-01	3.21e-01
chr11	98992943	99024189	ENSMUSG00000020914	0.4138	-	Top2a	protein_coding	1.08e-04	1.67e-02	-1.535648	8.78e+00	7.57e-02	7.53e+00	2.60e-02	8.35e+00	1.03e+01	7.69e+00	4.15e-02	1.59e-01	2.63e-02	8.35e+00	6.74e+00	7.50e+00	4.16e-02	1.35e-02	2.30e-02
chr11	107703218	107716522	ENSMUSG00000020722	0.4917	-	Cacng1	protein_coding	1.10e-04	1.69e-02	3.741467	1.11e+00	9.80e-04	3.66e+00	2.04e-02	0.00e+00	1.00e+00	2.32e+00	0.00e+00	5.88e-04	2.35e-03	0.00e+00	6.34e+00	4.64e+00	0.00e+00	4.71e-02	1.41e-02
chr5	61808816	61811163	ENSMUSG00000089992	0.4936	+	G6pd2	protein_coding	1.12e-04	1.70e-02	-1.715521	7.40e+00	1.15e-01	5.82e+00	3.45e-02	8.16e+00	7.94e+00	6.09e+00	1.65e-01	1.41e-01	3.86e-02	6.07e+00	6.39e+00	5.00e+00	3.80e-02	4.78e-02	1.78e-02
chr7	31068172	31076704	ENSMUSG00000057092	0.5179	-	Fxyd3	protein_coding	1.20e-04	1.79e-02	1.625666	7.12e+00	1.85e-02	8.60e+00	5.72e-02	6.81e+00	6.97e+00	7.60e+00	1.44e-02	1.60e-02	2.50e-02	9.60e+00	8.25e+00	7.95e+00	1.01e-01	3.93e-02	3.18e-02
chr11	67238029	67260446	ENSMUSG00000057003	0.4334	+	Myh4	protein_coding	1.21e-04	1.79e-02	1.870194	8.20e+00	3.00e-01	8.84e+00	1.10e+00	6.43e+00	8.62e+00	9.55e+00	6.25e-02	2.88e-01	5.51e-01	5.32e+00	1.19e+01	9.29e+00	2.87e-02	2.81e+00	4.58e-01
chr11	95509845	95514570	ENSMUSG00000046719	0.5908	-	Nxph3	protein_coding	1.22e-04	1.79e-02	-1.857981	6.98e+00	6.44e-02	5.12e+00	1.74e-02	7.68e+00	7.44e+00	5.81e+00	9.12e-02	7.73e-02	2.46e-02	4.25e+00	6.11e+00	5.00e+00	8.05e-03	3.04e-02	1.39e-02
chr11	98383811	98384953	ENSMUSG00000007877	0.6037	+	Tcap	protein_coding	1.24e-04	1.81e-02	1.803547	5.74e+00	5.38e-02	6.82e+00	1.89e-01	3.17e+00	7.16e+00	6.89e+00	4.81e-03	8.54e-02	7.10e-02	3.32e+00	9.54e+00	7.60e+00	5.42e-03	4.46e-01	1.16e-01
chr17	71458327	71460395	ENSMUSG00000091831	0.4838	-	Gm4707	protein_coding	1.38e-04	1.99e-02	-1.527202	9.19e+00	1.71e-01	7.74e+00	5.90e-02	9.68e+00	9.52e+00	8.37e+00	2.23e-01	1.99e-01	8.98e-02	8.09e+00	7.72e+00	7.41e+00	7.40e-02	5.71e-02	4.60e-02
chr9	54764748	54773110	ENSMUSG00000032291	0.4814	+	Crabp1	protein_coding	1.40e-04	2.00e-02	-2.309597	5.88e+00	5.15e-02	3.93e+00	9.86e-03	5.98e+00	7.21e+00	4.46e+00	4.17e-02	9.88e-02	1.41e-02	3.46e+00	4.00e+00	4.32e+00	6.72e-03	1.01e-02	1.28e-02
chr9	107296026	107302784	ENSMUSG00000032578	0.5261	+	Cish	protein_coding	1.42e-04	2.01e-02	2.000000	5.07e+00	9.59e-03	6.91e+00	3.92e-02	4.46e+00	5.13e+00	5.61e+00	5.87e-03	9.50e-03	1.34e-02	6.81e+00	7.96e+00	5.98e+00	3.10e-02	6.93e-02	1.73e-02
chr14	54642161	54686931	ENSMUSG00000022185	0.4559	-	Acin1	protein_coding	1.44e-04	2.01e-02	-1.400123	8.97e+00	3.10e-02	7.70e+00	1.17e-02	8.32e+00	8.74e+00	9.85e+00	1.78e-02	2.39e-02	5.13e-02	7.43e+00	7.66e+00	8.01e+00	9.55e-03	1.12e-02	1.44e-02
chr7	142757236	142757852	ENSMUSG00000078192	0.5689	-	Gm7290	processed_pseudogene	1.46e-04	2.02e-02	-1.635752	1.05e+01	1.42e+00	8.97e+00	4.57e-01	1.14e+01	1.11e+01	8.93e+00	2.15e+00	1.72e+00	3.88e-01	9.98e+00	8.57e+00	8.37e+00	8.04e-01	3.03e-01	2.63e-01
chr14	20649102	20656540	ENSMUSG00000068697	0.4757	-	Myoz1	protein_coding	1.50e-04	2.05e-02	2.091315	3.93e+00	1.85e-02	4.85e+00	8.10e-02	1.00e+00	5.67e+00	5.13e+00	6.54e-04	3.27e-02	2.22e-02	0.00e+00	8.19e+00	6.36e+00	0.00e+00	1.90e-01	5.29e-02

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

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.