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 (25361 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 (3718 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 0.07863 average reads per 100 bp), iii) genes with read counts below the median read counts of the total normalized count distribution (11128 genes with cutoff value 18 normalized read counts). The total number of genes excluded due to the application of gene filters was 6155. The total (unified) number of genes excluded due to the application of all filters was 37615. The resulting gene counts table was subjected to differential expression analysis for the contrasts Prox versus GFP using the Bioconductor package DESeq. The final numbers of differentially expressed genes were (per contrast): for the contrast Prox versus GFP, 1860 (551) statistically significant genes were found with a p-value (FDR or adjusted p-value) threshold of 0.05 and of these, 733 (323) were up-regulated, 640 (228) were down-regulated and 487 (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: Prox, GFP
Samples included: MF3R1_Prox1-1, MF3R2_Prox1-2, MF3R4_GFP1, MF3R5_GFP2
Samples excluded: none
Requested contrasts: Prox_vs_GFP
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/run418/wwwMFlab/metaseqr_quantseq_run340v2
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 42 seconds

Filtered genes

Number of filtered genes: 37615 which is the union of

Filtered because of zero reads: 25361
Filtered because of exon filters: 0
Filtered because of gene filters: 12254 which is the union of
- length: 3718 genes with filter cutoff value 500
- avg.reads: 2347 genes with filter cutoff value 0.07862794
- expression: 11128 genes further decomposed to (filter name, filtered genes, filter cutoff):
- - median: 11128 genes with filter cutoff value 18
- biotype: 89 genes with filter cutoff value rRNA, IG_V_pseudogene, TR_V_pseudogene

Differentially expressed genes

Number of differentially expressed genes per contrast:

Prox_vs_GFP: 1860 (551) statistically significant genes of which 733 (323) up regulated, 640 (228) down regulated and 487 (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

[ 2019-05-22 15:38:42 ] INFO 2019-05-22 15:38:42: Data processing started...
[ 2019-05-22 15:38:42 ] INFO Read counts file: imported custom data frame
[ 2019-05-22 15:38:42 ] INFO Conditions: Prox, GFP
[ 2019-05-22 15:38:42 ] INFO Samples to include: MF3R1_Prox1-1, MF3R2_Prox1-2, MF3R4_GFP1, MF3R5_GFP2
[ 2019-05-22 15:38:42 ] INFO Samples to exclude: none
[ 2019-05-22 15:38:42 ] INFO Requested contrasts: Prox_vs_GFP
[ 2019-05-22 15:38:42 ] INFO Annotation: embedded
[ 2019-05-22 15:38:42 ] INFO Organism: custom
[ 2019-05-22 15:38:42 ] INFO Reference source: ensembl
[ 2019-05-22 15:38:42 ] INFO Count type: utr
[ 2019-05-22 15:38:42 ] INFO Exon filters: none applied
[ 2019-05-22 15:38:42 ] INFO Gene filters: length, avg.reads, expression, biotype
[ 2019-05-22 15:38:42 ] INFO length:
[ 2019-05-22 15:38:42 ] INFO length: 500
[ 2019-05-22 15:38:42 ] INFO avg.reads:
[ 2019-05-22 15:38:42 ] INFO average.per.bp: 100
[ 2019-05-22 15:38:42 ] INFO quantile: 0.25
[ 2019-05-22 15:38:42 ] INFO expression:
[ 2019-05-22 15:38:42 ] INFO median: TRUE
[ 2019-05-22 15:38:42 ] INFO mean: FALSE
[ 2019-05-22 15:38:42 ] INFO quantile: NA
[ 2019-05-22 15:38:42 ] INFO known: NA
[ 2019-05-22 15:38:42 ] INFO custom: NA
[ 2019-05-22 15:38:42 ] INFO biotype:
[ 2019-05-22 15:38:42 ] INFO pseudogene: FALSE
[ 2019-05-22 15:38:42 ] INFO snRNA: FALSE
[ 2019-05-22 15:38:42 ] INFO protein_coding: FALSE
[ 2019-05-22 15:38:42 ] INFO antisense: FALSE
[ 2019-05-22 15:38:42 ] INFO miRNA: FALSE
[ 2019-05-22 15:38:42 ] INFO snoRNA: FALSE
[ 2019-05-22 15:38:42 ] INFO lincRNA: FALSE
[ 2019-05-22 15:38:42 ] INFO processed_transcript: FALSE
[ 2019-05-22 15:38:42 ] INFO misc_RNA: FALSE
[ 2019-05-22 15:38:42 ] INFO rRNA: TRUE
[ 2019-05-22 15:38:42 ] INFO sense_intronic: FALSE
[ 2019-05-22 15:38:42 ] INFO sense_overlapping: FALSE
[ 2019-05-22 15:38:42 ] INFO polymorphic_pseudogene: FALSE
[ 2019-05-22 15:38:42 ] INFO IG_C_gene: FALSE
[ 2019-05-22 15:38:42 ] INFO IG_J_gene: FALSE
[ 2019-05-22 15:38:42 ] INFO IG_D_gene: FALSE
[ 2019-05-22 15:38:42 ] INFO IG_LV_gene: FALSE
[ 2019-05-22 15:38:42 ] INFO IG_V_gene: FALSE
[ 2019-05-22 15:38:42 ] INFO IG_V_pseudogene: TRUE
[ 2019-05-22 15:38:42 ] INFO TR_V_gene: FALSE
[ 2019-05-22 15:38:42 ] INFO TR_V_pseudogene: TRUE
[ 2019-05-22 15:38:42 ] INFO three_prime_overlapping_ncrna: FALSE
[ 2019-05-22 15:38:42 ] INFO Filter application: postnorm
[ 2019-05-22 15:38:42 ] INFO Normalization algorithm: deseq
[ 2019-05-22 15:38:42 ] INFO Normalization arguments:
[ 2019-05-22 15:38:42 ] INFO locfunc:
[ 2019-05-22 15:38:42 ] INFO [[
[ 2019-05-22 15:38:42 ] INFO list(locfunc = function (x, na.rm = FALSE) standardGeneric("median"))
[ 2019-05-22 15:38:42 ] INFO locfunc
[ 2019-05-22 15:38:42 ] INFO Statistical algorithm: deseq
[ 2019-05-22 15:38:42 ] INFO Statistical arguments:
[ 2019-05-22 15:38:42 ] INFO deseq: blind, fit-only, local
[ 2019-05-22 15:38:42 ] INFO Meta-analysis method: none
[ 2019-05-22 15:38:42 ] INFO Multiple testing correction: BH
[ 2019-05-22 15:38:42 ] INFO p-value threshold: 0.05
[ 2019-05-22 15:38:42 ] INFO Logarithmic transformation offset: 1
[ 2019-05-22 15:38:42 ] INFO Quality control plots: mds, biodetection, countsbio, saturation, readnoise, filtered, correl, pairwise, boxplot, gcbias, lengthbias, meandiff, meanvar, biodist, volcano, deheatmap
[ 2019-05-22 15:38:42 ] INFO Figure format: png, pdf
[ 2019-05-22 15:38:42 ] INFO Output directory: /data/images/proton2/run418/wwwMFlab/metaseqr_quantseq_run340v2
[ 2019-05-22 15:38:42 ] INFO Output data: annotation, p.value, adj.p.value, fold.change, stats, counts, flags
[ 2019-05-22 15:38:42 ] INFO Output scale(s): log2, rpgm
[ 2019-05-22 15:38:42 ] INFO Output values: normalized
[ 2019-05-22 15:38:42 ] INFO Output statistics: mean
[ 2019-05-22 15:38:42 ] INFO Reading custom external gene annotation for from /data/results/tools/rnaseq/metaseqr/mm10/gene_data_mm10.txt.gz...
[ 2019-05-22 15:38:43 ] INFO Checking chromosomes in transcript counts and gene annotation...
[ 2019-05-22 15:38:43 ] INFO Processing transcripts...
[ 2019-05-22 15:38:44 ] INFO Separating transcripts (UTR regions) per gene for MF3R1_Prox1-1...
[ 2019-05-22 15:39:15 ] INFO Separating transcripts (UTR regions) per gene for MF3R2_Prox1-2...
[ 2019-05-22 15:39:46 ] INFO Separating transcripts (UTR regions) per gene for MF3R4_GFP1...
[ 2019-05-22 15:40:11 ] INFO Separating transcripts (UTR regions) per gene for MF3R5_GFP2...
[ 2019-05-22 15:40:40 ] INFO Saving gene model to /data/images/proton2/run418/wwwMFlab/metaseqr_quantseq_run340v2/data/gene_model.RData
[ 2019-05-22 15:40:41 ] INFO Summarizing count data...
[ 2019-05-22 15:40:44 ] INFO Removing genes with zero counts in all samples...
[ 2019-05-22 15:40:44 ] INFO Normalizing with: deseq
[ 2019-05-22 15:40:44 ] INFO Applying gene filter length...
[ 2019-05-22 15:40:45 ] INFO Threshold below which ignored: 500
[ 2019-05-22 15:40:45 ] INFO Applying gene filter avg.reads...
[ 2019-05-22 15:40:45 ] INFO Threshold below which ignored: 0.0786279441139976
[ 2019-05-22 15:40:45 ] INFO Applying gene filter expression...
[ 2019-05-22 15:40:45 ] INFO Threshold below which ignored: 18
[ 2019-05-22 15:40:45 ] INFO Applying gene filter biotype...
[ 2019-05-22 15:40:45 ] INFO Biotypes ignored: rRNA, IG_V_pseudogene, TR_V_pseudogene
[ 2019-05-22 15:40:45 ] INFO 37615 genes filtered out
[ 2019-05-22 15:40:45 ] INFO 11933 genes remain after filtering
[ 2019-05-22 15:40:45 ] INFO Running statistical tests with: deseq
[ 2019-05-22 15:40:45 ] INFO Contrast: Prox_vs_GFP
[ 2019-05-22 15:40:58 ] INFO Contrast Prox_vs_GFP: found 1860 genes
[ 2019-05-22 15:40:58 ] INFO Building output files...
[ 2019-05-22 15:41:01 ] INFO Contrast: Prox_vs_GFP
[ 2019-05-22 15:41:01 ] INFO Adding non-filtered data...
[ 2019-05-22 15:41:01 ] INFO binding annotation...
[ 2019-05-22 15:41:01 ] INFO binding p-values...
[ 2019-05-22 15:41:01 ] INFO binding FDRs...
[ 2019-05-22 15:41:02 ] INFO binding log2 normalized fold changes...
[ 2019-05-22 15:41:02 ] INFO binding normalized mean counts...
[ 2019-05-22 15:41:02 ] INFO binding normalized mean counts...
[ 2019-05-22 15:41:02 ] INFO binding all normalized counts for Prox...
[ 2019-05-22 15:41:02 ] INFO binding all normalized counts for GFP...
[ 2019-05-22 15:41:03 ] INFO binding filtering flags...
[ 2019-05-22 15:41:03 ] INFO Writing output...
[ 2019-05-22 15:41:03 ] INFO Adding filtered data...
[ 2019-05-22 15:41:03 ] INFO binding annotation...
[ 2019-05-22 15:41:03 ] INFO binding p-values...
[ 2019-05-22 15:41:03 ] INFO binding FDRs...
[ 2019-05-22 15:41:04 ] INFO binding log2 normalized fold changes...
[ 2019-05-22 15:41:04 ] INFO binding normalized mean counts...
[ 2019-05-22 15:41:05 ] INFO binding normalized mean counts...
[ 2019-05-22 15:41:06 ] INFO binding all normalized counts for Prox...
[ 2019-05-22 15:41:06 ] INFO binding all normalized counts for GFP...
[ 2019-05-22 15:41:06 ] INFO binding filtering flags...
[ 2019-05-22 15:41:06 ] INFO Writing output...
[ 2019-05-22 15:41:08 ] INFO Creating quality control graphs...
[ 2019-05-22 15:41:08 ] INFO Plotting in png format...
[ 2019-05-22 15:41:08 ] INFO Plotting mds...
[ 2019-05-22 15:41:08 ] INFO Plotting biodetection...
[ 2019-05-22 15:41:09 ] INFO Plotting countsbio...
[ 2019-05-22 15:41:10 ] INFO Plotting saturation...
[ 2019-05-22 15:41:15 ] INFO Plotting readnoise...
[ 2019-05-22 15:41:15 ] INFO Plotting correl...
[ 2019-05-22 15:41:15 ] INFO Plotting pairwise...
[ 2019-05-22 15:41:19 ] INFO Plotting boxplot...
[ 2019-05-22 15:41:19 ] INFO Plotting gcbias...
[ 2019-05-22 15:41:19 ] INFO Plotting lengthbias...
[ 2019-05-22 15:41:20 ] INFO Plotting meandiff...
[ 2019-05-22 15:41:20 ] INFO Plotting meanvar...
[ 2019-05-22 15:41:22 ] INFO Plotting boxplot...
[ 2019-05-22 15:41:22 ] INFO Plotting gcbias...
[ 2019-05-22 15:41:22 ] INFO Plotting lengthbias...
[ 2019-05-22 15:41:22 ] INFO Plotting meandiff...
[ 2019-05-22 15:41:23 ] INFO Plotting meanvar...
[ 2019-05-22 15:41:25 ] INFO Plotting biodist...
[ 2019-05-22 15:41:25 ] INFO Contrast: Prox_vs_GFP
[ 2019-05-22 15:41:25 ] INFO Plotting volcano...
[ 2019-05-22 15:41:25 ] INFO Contrast: Prox_vs_GFP
[ 2019-05-22 15:41:25 ] INFO Plotting deheatmap...
[ 2019-05-22 15:41:25 ] INFO Contrast: Prox_vs_GFP
[ 2019-05-22 15:41:49 ] INFO Plotting filtered...
[ 2019-05-22 15:41:49 ] INFO Plotting in pdf format...
[ 2019-05-22 15:41:49 ] INFO Plotting mds...
[ 2019-05-22 15:41:49 ] INFO Plotting biodetection...
[ 2019-05-22 15:41:50 ] INFO Plotting countsbio...
[ 2019-05-22 15:41:50 ] INFO Plotting saturation...
[ 2019-05-22 15:41:53 ] INFO Plotting readnoise...
[ 2019-05-22 15:41:54 ] INFO Plotting correl...
[ 2019-05-22 15:41:54 ] INFO Plotting pairwise...
[ 2019-05-22 15:41:56 ] INFO Plotting boxplot...
[ 2019-05-22 15:41:56 ] INFO Plotting gcbias...
[ 2019-05-22 15:41:56 ] INFO Plotting lengthbias...
[ 2019-05-22 15:41:57 ] INFO Plotting meandiff...
[ 2019-05-22 15:41:57 ] INFO Plotting meanvar...
[ 2019-05-22 15:41:59 ] INFO Plotting boxplot...
[ 2019-05-22 15:41:59 ] INFO Plotting gcbias...
[ 2019-05-22 15:41:59 ] INFO Plotting lengthbias...
[ 2019-05-22 15:42:00 ] INFO Plotting meandiff...
[ 2019-05-22 15:42:00 ] INFO Plotting meanvar...
[ 2019-05-22 15:42:02 ] INFO Plotting biodist...
[ 2019-05-22 15:42:02 ] INFO Contrast: Prox_vs_GFP
[ 2019-05-22 15:42:02 ] INFO Plotting volcano...
[ 2019-05-22 15:42:02 ] INFO Contrast: Prox_vs_GFP
[ 2019-05-22 15:42:02 ] INFO Plotting deheatmap...
[ 2019-05-22 15:42:02 ] INFO Contrast: Prox_vs_GFP
[ 2019-05-22 15:42:24 ] INFO Plotting filtered...
[ 2019-05-22 15:42:24 ] INFO Creating HTML report...
[ 2019-05-22 15:42:24 ] 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 Prox

Mean-difference plots for the replicates of GFP

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 Prox

Mean-variance plots for the replicates of GFP

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 Prox_vs_GFP

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 Prox_vs_GFP

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 Prox_vs_GFP

Get all the figures in png, pdf format.

Results

DEG table for the contrast Prox_vs_GFP

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 Prox_vs_GFP. 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_Prox_vs_GFP	log2_normalized_mean_counts_Prox	rpgm_normalized_mean_counts_Prox	log2_normalized_mean_counts_GFP	rpgm_normalized_mean_counts_GFP	log2_normalized_counts_MF3R1_Prox1-1	log2_normalized_counts_MF3R2_Prox1-2	rpgm_normalized_counts_MF3R1_Prox1-1	rpgm_normalized_counts_MF3R2_Prox1-2	log2_normalized_counts_MF3R4_GFP1	log2_normalized_counts_MF3R5_GFP2	rpgm_normalized_counts_MF3R4_GFP1	rpgm_normalized_counts_MF3R5_GFP2
chr6	17463800	17573980	ENSMUSG00000009376	0.4179	+	Met	protein_coding	1.55e-57	1.85e-53	12.857787	0.00e+00	0.00e+00	8.66e+00	8.77e-01	0.00e+00	0.00e+00	0.00e+00	0.00e+00	3.46e+00	1.39e+01	1.18e-03	1.75e+00
chr5	75574916	75656722	ENSMUSG00000005672	0.4601	+	Kit	protein_coding	1.62e-51	9.67e-48	10.526988	0.00e+00	0.00e+00	1.03e+01	1.52e-01	0.00e+00	0.00e+00	0.00e+00	0.00e+00	1.11e+01	9.52e+00	2.28e-01	7.57e-02
chr5	114948980	114971094	ENSMUSG00000029556	0.5183	-	Hnf1a	protein_coding	2.41e-50	9.60e-47	9.332596	1.00e+00	8.22e-05	9.92e+00	1.06e-01	1.00e+00	1.00e+00	8.22e-05	8.22e-05	8.79e+00	1.11e+01	3.63e-02	1.76e-01
chr1	190118035	190170714	ENSMUSG00000010175	0.4514	-	Prox1	protein_coding	2.06e-47	6.16e-44	-9.088599	1.08e+01	2.96e-01	1.79e+00	3.88e-04	1.03e+01	1.13e+01	2.03e-01	3.89e-01	1.58e+00	2.00e+00	3.11e-04	4.66e-04
chr9	3000282	3038313	ENSMUSG00000097312	0.3488	-	Gm26870	lincRNA	6.40e-33	1.53e-29	-6.285402	9.69e+00	2.38e-01	3.40e+00	2.79e-03	1.03e+01	9.11e+00	3.30e-01	1.46e-01	2.81e+00	4.00e+00	1.59e-03	3.99e-03
chr2	31688376	31804227	ENSMUSG00000026842	0.4686	+	Abl1	protein_coding	5.68e-29	1.13e-25	7.280709	6.23e+00	3.39e-03	1.18e+01	5.34e-01	6.29e+00	6.17e+00	3.53e-03	3.25e-03	1.45e+01	9.12e+00	1.04e+00	2.55e-02
chr8	72723285	72758201	ENSMUSG00000031622	0.5008	+	Sin3b	protein_coding	2.39e-21	4.07e-18	7.409391	0.00e+00	0.00e+00	7.41e+00	1.93e-02	0.00e+00	0.00e+00	0.00e+00	0.00e+00	7.38e+00	7.43e+00	1.90e-02	1.96e-02
chrX	167207093	167209315	ENSMUSG00000049775	0.4777	-	Tmsb4x	protein_coding	3.56e-21	5.31e-18	4.733354	3.61e+00	6.92e-03	7.52e+00	1.99e-01	3.91e+00	3.32e+00	8.42e-03	5.42e-03	5.78e+00	9.25e+00	3.25e-02	3.66e-01
chr14	73183673	73325822	ENSMUSG00000022105	0.3921	-	Rb1	protein_coding	7.55e-21	1.00e-17	4.978889	7.00e+00	1.39e-02	1.00e+01	4.41e-01	6.97e+00	7.03e+00	1.35e-02	1.42e-02	1.30e+01	7.11e+00	8.66e-01	1.50e-02
chr1	68032186	69108059	ENSMUSG00000062209	0.3602	-	Erbb4	protein_coding	2.93e-20	3.50e-17	7.238405	0.00e+00	0.00e+00	6.99e+00	1.17e-02	0.00e+00	0.00e+00	0.00e+00	0.00e+00	6.11e+00	7.86e+00	5.28e-03	1.80e-02
chr1	120006894	120063536	ENSMUSG00000026387	0.4806	+	Sctr	protein_coding	3.57e-20	3.88e-17	4.194378	5.28e+00	2.52e-02	9.48e+00	4.73e-01	5.43e+00	5.13e+00	2.79e-02	2.25e-02	9.55e+00	9.40e+00	4.97e-01	4.48e-01
chr10	95417375	95428640	ENSMUSG00000097766	0.4526	+	5730420D15Rik	lincRNA	1.23e-18	1.22e-15	-4.385862	8.34e+00	2.66e-01	3.89e+00	1.20e-02	8.71e+00	7.96e+00	3.34e-01	1.98e-01	4.46e+00	3.32e+00	1.67e-02	7.18e-03
chr1	65158616	65186500	ENSMUSG00000025950	0.4252	-	Idh1	protein_coding	5.27e-17	4.66e-14	4.131875	7.15e+00	1.98e-02	1.01e+01	3.50e-01	7.34e+00	6.95e+00	2.25e-02	1.72e-02	1.22e+01	7.97e+00	6.65e-01	3.49e-02
chr13	23684199	23692488	ENSMUSG00000018102	0.4002	+	Hist1h2bc	protein_coding	5.71e-17	4.66e-14	-4.012087	8.68e+00	1.80e-01	4.68e+00	1.08e-02	8.99e+00	8.37e+00	2.19e-01	1.42e-01	4.46e+00	4.91e+00	9.05e-03	1.25e-02
chr10	22306702	22321765	ENSMUSG00000097352	0.4299	+	C920009B18Rik	transcribed_unprocessed_pseudogene	5.86e-17	4.66e-14	6.189825	7.92e-01	1.99e-04	7.19e+00	2.89e-02	0.00e+00	1.58e+00	0.00e+00	3.99e-04	7.29e+00	7.08e+00	3.11e-02	2.67e-02
chr3	32397671	32468486	ENSMUSG00000027665	0.4143	+	Pik3ca	protein_coding	1.87e-16	1.40e-13	4.151363	7.45e+00	2.53e-02	1.03e+01	4.52e-01	7.61e+00	7.29e+00	2.81e-02	2.26e-02	1.25e+01	8.06e+00	8.66e-01	3.85e-02
chr6	128483567	128526720	ENSMUSG00000030359	0.4161	-	Pzp	protein_coding	2.98e-16	2.09e-13	-5.209453	7.35e+00	3.21e-02	2.08e+00	6.79e-04	7.64e+00	7.07e+00	3.84e-02	2.58e-02	2.58e+00	1.58e+00	9.70e-04	3.88e-04
chr2	98666547	98667301	ENSMUSG00000075014	0.3563	-	Gm10800	protein_coding	6.44e-15	4.27e-12	-6.027906	7.01e+00	1.03e-01	7.92e-01	7.97e-04	7.25e+00	6.77e+00	1.20e-01	8.61e-02	0.00e+00	1.58e+00	0.00e+00	1.59e-03
chr16	93792150	93794888	ENSMUSG00000097768	0.5027	-	2310043M15Rik	lincRNA	5.90e-14	3.59e-11	-5.124121	7.10e+00	1.10e-01	1.95e+00	2.39e-03	6.82e+00	7.38e+00	8.93e-02	1.32e-01	2.32e+00	1.58e+00	3.19e-03	1.59e-03
chr2	60292103	60383303	ENSMUSG00000026980	0.4249	-	Ly75	protein_coding	6.02e-14	3.59e-11	5.113742	2.00e+00	1.03e-03	7.11e+00	4.72e-02	2.00e+00	2.00e+00	1.03e-03	1.03e-03	7.11e+00	7.12e+00	4.71e-02	4.74e-02
chr4	33132521	33163588	ENSMUSG00000028280	0.4625	+	Gabrr1	protein_coding	7.70e-14	4.37e-11	-5.159871	7.16e+00	7.23e-02	1.95e+00	1.53e-03	7.16e+00	7.16e+00	7.23e-02	7.23e-02	1.58e+00	2.32e+00	1.02e-03	2.04e-03
chr8	123952459	123983122	ENSMUSG00000031974	0.4923	-	Abcb10	protein_coding	1.10e-13	5.99e-11	-3.153130	1.01e+01	3.44e-01	6.90e+00	3.83e-02	1.02e+01	9.88e+00	3.84e-01	3.04e-01	7.08e+00	6.71e+00	4.32e-02	3.35e-02
chr13	23738808	23740367	ENSMUSG00000036181	0.5506	+	Hist1h1c	protein_coding	2.79e-13	1.45e-10	-3.118338	9.39e+00	3.58e-01	6.26e+00	4.07e-02	9.28e+00	9.50e+00	3.29e-01	3.86e-01	6.46e+00	6.07e+00	4.63e-02	3.51e-02
chr8	5436984	5440990	ENSMUSG00000109313	0.3908	-	Gm44693	lincRNA	3.35e-13	1.67e-10	-6.014950	6.58e+00	7.66e-02	5.00e-01	3.99e-04	6.32e+00	6.83e+00	6.30e-02	9.01e-02	1.00e+00	0.00e+00	7.97e-04	0.00e+00
chr5	30013114	30019981	ENSMUSG00000025746	0.4078	+	Il6	protein_coding	1.41e-12	6.71e-10	4.435819	2.81e+00	2.05e-03	7.24e+00	5.15e-02	2.81e+00	2.81e+00	2.05e-03	2.05e-03	7.26e+00	7.23e+00	5.20e-02	5.10e-02
chr11	63128982	63159547	ENSMUSG00000018217	0.4813	+	Pmp22	protein_coding	1.13e-11	5.16e-09	2.839640	7.24e+00	4.13e-02	1.01e+01	2.98e-01	7.10e+00	7.38e+00	3.74e-02	4.53e-02	1.01e+01	1.01e+01	2.96e-01	3.00e-01
chr2	35313986	35336976	ENSMUSG00000026880	0.4638	-	Stom	protein_coding	2.45e-11	1.08e-08	3.086360	5.20e+00	1.20e-02	8.35e+00	1.04e-01	4.75e+00	5.64e+00	8.31e-03	1.57e-02	8.33e+00	8.38e+00	1.03e-01	1.06e-01
chr6	29433256	29461883	ENSMUSG00000068699	0.5513	+	Flnc	protein_coding	6.64e-11	2.83e-08	4.946419	1.58e+00	6.29e-04	6.49e+00	2.88e-02	1.58e+00	1.58e+00	6.29e-04	6.29e-04	6.13e+00	6.85e+00	2.17e-02	3.58e-02
chr17	42656891	42692284	ENSMUSG00000023918	0.4222	-	Adgrf4	protein_coding	8.70e-11	3.58e-08	5.330917	7.92e-01	2.37e-04	6.31e+00	1.88e-02	0.00e+00	1.58e+00	0.00e+00	4.73e-04	6.07e+00	6.55e+00	1.56e-02	2.20e-02
chr11	100368958	100397763	ENSMUSG00000001552	0.5225	-	Jup	protein_coding	1.24e-10	4.94e-08	-2.866998	8.27e+00	4.41e-02	5.38e+00	5.93e-03	8.67e+00	7.88e+00	5.59e-02	3.24e-02	5.86e+00	4.91e+00	7.86e-03	4.00e-03
chr5	146582398	146585239	ENSMUSG00000041468	0.5218	-	Gpr12	protein_coding	1.53e-10	5.73e-08	3.146418	4.78e+00	7.79e-03	7.93e+00	7.13e-02	4.64e+00	4.91e+00	7.06e-03	8.53e-03	7.96e+00	7.89e+00	7.29e-02	6.97e-02
chr8	121666814	121674088	ENSMUSG00000110673	0.5120	-	RP24-365B9.2	lincRNA	1.54e-10	5.73e-08	-2.693104	9.17e+00	4.69e-01	6.50e+00	7.18e-02	9.47e+00	8.87e+00	5.64e-01	3.73e-01	6.63e+00	6.38e+00	7.81e-02	6.54e-02
chr1	151571186	151721939	ENSMUSG00000026483	0.4375	+	Fam129a	protein_coding	1.59e-10	5.73e-08	2.942515	5.32e+00	3.91e-03	8.26e+00	3.07e-02	5.29e+00	5.36e+00	3.81e-03	4.01e-03	8.13e+00	8.39e+00	2.80e-02	3.35e-02
chr7	25802475	25816913	ENSMUSG00000040703	0.5118	-	Cyp2s1	protein_coding	3.44e-10	1.21e-07	4.815917	1.58e+00	3.87e-04	6.33e+00	1.62e-02	1.58e+00	1.58e+00	3.87e-04	3.87e-04	5.86e+00	6.79e+00	1.10e-02	2.13e-02
chr11	96464593	96759130	ENSMUSG00000057058	0.4355	+	Skap1	protein_coding	3.99e-10	1.36e-07	-3.207595	7.60e+00	1.52e-02	4.31e+00	1.58e-03	7.65e+00	7.55e+00	1.58e-02	1.47e-02	4.81e+00	3.81e+00	2.13e-03	1.03e-03
chr10	10686059	11082356	ENSMUSG00000019828	0.3951	-	Grm1	protein_coding	6.20e-10	2.05e-07	3.282693	4.34e+00	2.26e-03	7.63e+00	2.30e-02	4.58e+00	4.09e+00	2.67e-03	1.85e-03	7.83e+00	7.42e+00	2.63e-02	1.97e-02
chr17	29318877	29329413	ENSMUSG00000024011	0.5406	+	Pi16	protein_coding	7.42e-10	2.39e-07	2.608152	6.16e+00	2.61e-02	8.76e+00	1.61e-01	6.02e+00	6.30e+00	2.36e-02	2.87e-02	8.55e+00	8.97e+00	1.38e-01	1.84e-01
chr12	3962554	4038915	ENSMUSG00000020658	0.4751	-	Efr3b	protein_coding	8.61e-10	2.66e-07	-2.598755	9.99e+00	2.04e-01	7.42e+00	3.35e-02	1.04e+01	9.61e+00	2.57e-01	1.52e-01	7.18e+00	7.66e+00	2.80e-02	3.91e-02
chr1	107687008	107693514	ENSMUSG00000086161	0.4105	+	Gm15389	lincRNA	8.68e-10	2.66e-07	-5.481127	6.07e+00	5.26e-02	5.00e-01	3.99e-04	6.04e+00	6.09e+00	5.18e-02	5.34e-02	1.00e+00	0.00e+00	7.97e-04	0.00e+00
chrX	78449613	78583896	ENSMUSG00000047996	0.3731	-	Prrg1	protein_coding	1.50e-09	4.48e-07	-5.857981	5.82e+00	4.80e-03	0.00e+00	0.00e+00	5.49e+00	6.15e+00	3.71e-03	5.90e-03	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr7	82966589	82967217	ENSMUSG00000109541	0.4436	-	Gm6112	processed_pseudogene	1.63e-09	4.65e-07	-5.845490	5.83e+00	4.51e-02	0.00e+00	0.00e+00	6.02e+00	5.64e+00	5.10e-02	3.91e-02	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chrX	166238911	166388988	ENSMUSG00000031342	0.4179	+	Gpm6b	protein_coding	1.64e-09	4.65e-07	2.584963	6.35e+00	6.20e-03	8.93e+00	3.76e-02	6.41e+00	6.29e+00	6.47e-03	5.93e-03	9.03e+00	8.83e+00	4.01e-02	3.50e-02
chr6	71463311	71466062	ENSMUSG00000107460	0.3877	-	Gm44186	TEC	1.75e-09	4.87e-07	-4.643856	6.20e+00	5.90e-02	1.16e+00	1.59e-03	6.49e+00	5.91e+00	7.10e-02	4.70e-02	2.32e+00	0.00e+00	3.19e-03	0.00e+00
chr7	83631959	83653127	ENSMUSG00000046027	0.4810	+	Stard5	protein_coding	1.96e-09	5.33e-07	2.809897	5.31e+00	4.36e-03	8.15e+00	3.12e-02	5.61e+00	5.00e+00	5.30e-03	3.42e-03	8.22e+00	8.08e+00	3.28e-02	2.97e-02
chr1	173920407	173942672	ENSMUSG00000039997	0.3794	-	Ifi203	protein_coding	3.71e-09	9.83e-07	-4.857981	6.18e+00	1.00e-02	1.29e+00	2.10e-04	6.17e+00	6.19e+00	9.94e-03	1.01e-02	1.58e+00	1.00e+00	2.80e-04	1.40e-04
chr2	36216777	36222934	ENSMUSG00000087187	0.4933	+	Gm13431	lincRNA	4.05e-09	1.05e-06	5.415037	5.00e-01	1.94e-04	5.90e+00	2.44e-02	1.00e+00	0.00e+00	3.88e-04	0.00e+00	6.44e+00	5.36e+00	3.34e-02	1.55e-02
chrX	9033493	9034333	ENSMUSG00000071815	0.5208	+	Fthl17e	protein_coding	4.81e-09	1.22e-06	-5.768184	5.75e+00	4.60e-02	0.00e+00	0.00e+00	5.55e+00	5.95e+00	3.95e-02	5.24e-02	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr7	99153658	99182719	ENSMUSG00000030747	0.5213	-	Dgat2	protein_coding	4.98e-09	1.24e-06	-2.718618	8.22e+00	3.21e-02	5.51e+00	4.79e-03	8.14e+00	8.31e+00	3.03e-02	3.40e-02	5.43e+00	5.58e+00	4.52e-03	5.06e-03
chr13	4191184	4205596	ENSMUSG00000021213	0.3669	+	Akr1c13	protein_coding	7.35e-09	1.79e-06	-2.464609	8.87e+00	5.01e-02	6.46e+00	9.00e-03	9.30e+00	8.44e+00	6.46e-02	3.57e-02	6.38e+00	6.55e+00	8.43e-03	9.56e-03
chr19	24999529	25202432	ENSMUSG00000052085	0.4446	+	Dock8	protein_coding	9.68e-09	2.31e-06	5.159871	7.92e-01	3.79e-04	4.07e+00	2.67e-02	0.00e+00	1.58e+00	0.00e+00	7.58e-04	7.14e+00	1.00e+00	5.31e-02	3.79e-04
chrX	47809368	47892974	ENSMUSG00000031099	0.3747	-	Smarca1	protein_coding	1.09e-08	2.55e-06	-3.770518	6.82e+00	1.30e-02	3.02e+00	8.50e-04	7.16e+00	6.48e+00	1.61e-02	9.98e-03	2.58e+00	3.46e+00	5.67e-04	1.13e-03
chr19	32757497	32826160	ENSMUSG00000013663	0.3811	+	Pten	protein_coding	1.34e-08	3.08e-06	2.654821	8.60e+00	3.91e-02	1.09e+01	2.47e-01	8.64e+00	8.55e+00	4.03e-02	3.79e-02	1.20e+01	9.85e+00	4.00e-01	9.34e-02
chr17	15806253	15826586	ENSMUSG00000048027	0.4476	-	Rgmb	protein_coding	1.40e-08	3.16e-06	2.658026	5.26e+00	1.75e-02	7.90e+00	1.13e-01	5.13e+00	5.39e+00	1.58e-02	1.91e-02	7.64e+00	8.16e+00	9.23e-02	1.33e-01
chr17	65923066	65951187	ENSMUSG00000024098	0.4498	-	Twsg1	protein_coding	1.44e-08	3.18e-06	-2.324745	9.58e+00	1.72e-01	7.26e+00	3.41e-02	9.74e+00	9.41e+00	1.91e-01	1.52e-01	7.33e+00	7.19e+00	3.58e-02	3.24e-02
chr4	101717404	101815352	ENSMUSG00000057722	0.4051	+	Lepr	protein_coding	1.49e-08	3.24e-06	-2.378968	9.22e+00	6.26e-02	6.91e+00	1.19e-02	9.71e+00	8.73e+00	8.32e-02	4.19e-02	6.70e+00	7.12e+00	1.02e-02	1.37e-02
chr7	62377010	62381640	ENSMUSG00000056972	0.5757	+	Magel2	protein_coding	2.31e-08	4.93e-06	-3.538420	6.92e+00	8.43e-02	3.35e+00	6.62e-03	7.09e+00	6.75e+00	9.41e-02	7.46e-02	3.00e+00	3.70e+00	4.88e-03	8.36e-03
chr7	112427505	112591692	ENSMUSG00000030770	0.4648	+	Parva	protein_coding	3.06e-08	6.42e-06	2.288569	7.27e+00	1.01e-02	9.57e+00	4.98e-02	6.95e+00	7.58e+00	7.96e-03	1.23e-02	9.77e+00	9.38e+00	5.64e-02	4.31e-02
chr12	17690814	17791926	ENSMUSG00000071379	0.4907	+	Hpcal1	protein_coding	3.94e-08	8.10e-06	3.626783	3.08e+00	3.80e-03	6.71e+00	5.26e-02	3.00e+00	3.17e+00	3.54e-03	4.05e-03	6.81e+00	6.61e+00	5.62e-02	4.91e-02
chr17	50279394	50293599	ENSMUSG00000010592	0.3457	-	Dazl	protein_coding	5.28e-08	1.07e-05	2.962525	4.28e+00	5.95e-03	7.24e+00	4.86e-02	4.25e+00	4.32e+00	5.79e-03	6.11e-03	7.13e+00	7.36e+00	4.47e-02	5.24e-02
chr2	165111507	165234853	ENSMUSG00000053166	0.5252	-	Cdh22	protein_coding	6.05e-08	1.20e-05	5.539159	0.00e+00	0.00e+00	5.53e+00	6.45e-03	0.00e+00	0.00e+00	0.00e+00	0.00e+00	5.36e+00	5.70e+00	5.67e-03	7.23e-03
chr10	61738665	61784147	ENSMUSG00000020086	0.4784	-	H2afy2	protein_coding	6.35e-08	1.24e-05	3.798742	2.50e+00	2.65e-03	6.27e+00	4.37e-02	3.00e+00	2.00e+00	3.71e-03	1.59e-03	5.70e+00	6.85e+00	2.70e-02	6.04e-02
chr13	23583742	23621124	ENSMUSG00000047246	0.4169	-	Hist1h2be	protein_coding	7.02e-08	1.35e-05	-5.129283	5.71e+00	1.19e-02	5.00e-01	1.15e-04	5.64e+00	5.78e+00	1.13e-02	1.24e-02	1.00e+00	0.00e+00	2.30e-04	0.00e+00
chr11	116336353	116413032	ENSMUSG00000052949	0.4806	-	Rnf157	protein_coding	7.91e-08	1.50e-05	-2.259330	1.06e+01	1.38e-01	8.34e+00	2.88e-02	1.05e+01	1.07e+01	1.33e-01	1.43e-01	8.34e+00	8.35e+00	2.87e-02	2.89e-02
chr4	137468769	137570630	ENSMUSG00000028763	0.5392	+	Hspg2	protein_coding	9.11e-08	1.70e-05	2.798558	4.45e+00	1.01e-02	7.34e+00	7.32e-02	3.91e+00	5.00e+00	6.31e-03	1.40e-02	7.13e+00	7.55e+00	6.26e-02	8.38e-02
chr1	43123074	43196984	ENSMUSG00000008136	0.4475	-	Fhl2	protein_coding	9.69e-08	1.78e-05	-5.491853	3.73e+00	1.49e-02	0.00e+00	0.00e+00	6.46e+00	1.00e+00	2.95e-02	3.39e-04	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chrX	89403848	89409689	ENSMUSG00000035395	0.4177	-	Pet2	protein_coding	1.06e-07	1.92e-05	-3.175639	6.94e+00	7.45e-02	3.80e+00	7.72e-03	7.29e+00	6.60e+00	9.20e-02	5.70e-02	3.70e+00	3.91e+00	7.13e-03	8.31e-03
chr3	135281221	135304426	ENSMUSG00000028167	0.4564	+	Bdh2	protein_coding	1.15e-07	2.02e-05	2.941897	4.01e+00	6.63e-03	7.16e+00	5.34e-02	4.86e+00	3.17e+00	1.03e-02	2.95e-03	6.87e+00	7.45e+00	4.28e-02	6.41e-02
chr4	138775735	138782046	ENSMUSG00000041202	0.5109	+	Pla2g2d	protein_coding	1.17e-07	2.02e-05	3.532495	2.52e+00	2.06e-03	4.45e+00	2.75e-02	1.58e+00	3.46e+00	6.88e-04	3.44e-03	1.58e+00	7.31e+00	6.88e-04	5.44e-02
chr9	85320439	85327348	ENSMUSG00000032265	0.4436	-	Fam46a	protein_coding	1.18e-07	2.02e-05	2.410818	5.74e+00	9.96e-03	8.15e+00	5.38e-02	5.73e+00	5.75e+00	9.86e-03	1.01e-02	8.25e+00	8.05e+00	5.75e-02	5.01e-02
chr6	49822710	49829507	ENSMUSG00000029819	0.4440	+	Npy	protein_coding	1.18e-07	2.02e-05	3.495411	3.08e+00	5.58e-03	6.66e+00	7.01e-02	3.58e+00	2.58e+00	7.67e-03	3.49e-03	6.75e+00	6.57e+00	7.46e-02	6.56e-02
chr11	116008457	116012719	ENSMUSG00000020766	0.5658	-	Galk1	protein_coding	1.21e-07	2.03e-05	2.202805	8.90e+00	3.34e-01	1.11e+01	1.54e+00	9.07e+00	8.74e+00	3.72e-01	2.96e-01	1.06e+01	1.15e+01	1.11e+00	1.97e+00
chr11	72301629	72313733	ENSMUSG00000040483	0.4732	+	Xaf1	protein_coding	1.22e-07	2.03e-05	-2.243465	1.09e+01	2.84e-01	8.64e+00	5.98e-02	1.11e+01	1.06e+01	3.40e-01	2.28e-01	8.69e+00	8.60e+00	6.17e-02	5.79e-02
chr17	47394560	47400584	ENSMUSG00000023982	0.5114	-	Guca1a	protein_coding	1.51e-07	2.46e-05	2.608046	4.89e+00	2.22e-02	7.56e+00	1.39e-01	4.46e+00	5.32e+00	1.55e-02	2.89e-02	7.43e+00	7.69e+00	1.27e-01	1.52e-01
chr19	8989284	9076919	ENSMUSG00000069833	0.4665	+	Ahnak	protein_coding	1.63e-07	2.62e-05	2.218180	6.19e+00	1.95e-02	8.39e+00	9.18e-02	6.04e+00	6.34e+00	1.75e-02	2.15e-02	8.10e+00	8.68e+00	7.35e-02	1.10e-01
chr3	104788375	104794459	ENSMUSG00000002233	0.5527	+	Rhoc	protein_coding	1.65e-07	2.62e-05	2.139214	8.50e+00	1.17e-01	1.06e+01	5.15e-01	8.38e+00	8.63e+00	1.07e-01	1.27e-01	1.04e+01	1.08e+01	4.43e-01	5.86e-01
chr8	15011025	15033333	ENSMUSG00000055675	0.5174	+	Kbtbd11	protein_coding	1.83e-07	2.87e-05	3.443919	3.02e+00	1.15e-03	6.47e+00	1.40e-02	3.46e+00	2.58e+00	1.53e-03	7.65e-04	6.07e+00	6.88e+00	1.01e-02	1.79e-02
chr3	51388412	51396738	ENSMUSG00000037161	0.4453	-	Mgarp	protein_coding	2.02e-07	3.13e-05	2.152735	6.55e+00	2.38e-02	8.76e+00	1.07e-01	7.01e+00	6.09e+00	3.12e-02	1.63e-02	8.57e+00	8.95e+00	9.26e-02	1.20e-01
chr7	46644641	46672537	ENSMUSG00000040046	0.4476	-	Tph1	protein_coding	2.37e-07	3.63e-05	3.576925	2.88e+00	5.16e-04	6.48e+00	7.03e-03	2.58e+00	3.17e+00	3.97e-04	6.35e-04	6.58e+00	6.38e+00	7.55e-03	6.51e-03
chr2	165595032	165771727	ENSMUSG00000017897	0.4927	+	Eya2	protein_coding	2.94e-07	4.45e-05	-3.385431	6.69e+00	1.84e-02	3.31e+00	1.60e-03	6.92e+00	6.46e+00	2.13e-02	1.55e-02	3.17e+00	3.46e+00	1.42e-03	1.78e-03
chr12	87332488	87335655	ENSMUSG00000097667	0.3889	+	Gm26764	lincRNA	3.36e-07	5.01e-05	-4.226068	5.90e+00	5.14e-02	1.79e+00	1.99e-03	6.52e+00	5.29e+00	7.26e-02	3.03e-02	1.58e+00	2.00e+00	1.59e-03	2.39e-03
chr5	104046306	104065379	ENSMUSG00000029310	0.4563	+	Nudt9	protein_coding	3.46e-07	5.10e-05	-1.986803	8.63e+00	1.08e-01	7.18e+00	2.71e-02	1.00e+01	7.23e+00	1.89e-01	2.69e-02	7.58e+00	6.78e+00	3.45e-02	1.97e-02
chr8	3567990	3584939	ENSMUSG00000065952	0.4553	+	C330021F23Rik	protein_coding	3.60e-07	5.24e-05	-3.220048	6.65e+00	2.28e-02	3.36e+00	2.25e-03	6.94e+00	6.36e+00	2.74e-02	1.82e-02	3.91e+00	2.81e+00	3.15e-03	1.35e-03
chr19	10389090	10453181	ENSMUSG00000024743	0.5319	+	Syt7	protein_coding	3.85e-07	5.52e-05	2.040806	7.25e+00	2.53e-02	9.29e+00	1.05e-01	7.28e+00	7.23e+00	2.57e-02	2.49e-02	9.13e+00	9.44e+00	9.36e-02	1.16e-01
chr6	121210696	121226854	ENSMUSG00000030137	0.4933	+	Tuba8	protein_coding	3.88e-07	5.52e-05	2.063671	6.68e+00	5.00e-02	8.70e+00	2.11e-01	6.58e+00	6.77e+00	4.68e-02	5.32e-02	8.36e+00	9.04e+00	1.62e-01	2.60e-01
chrX	8892382	8894964	ENSMUSG00000052364	0.5757	+	B630019K06Rik	protein_coding	4.72e-07	6.63e-05	4.658211	7.92e-01	4.54e-04	5.65e+00	2.25e-02	1.58e+00	0.00e+00	9.07e-04	0.00e+00	5.52e+00	5.78e+00	2.04e-02	2.45e-02
chr16	13903161	13949619	ENSMUSG00000022679	0.4358	+	Mpv17l	protein_coding	4.92e-07	6.82e-05	-2.020591	9.62e+00	1.12e-01	7.64e+00	2.75e-02	9.96e+00	9.29e+00	1.38e-01	8.61e-02	7.76e+00	7.52e+00	2.99e-02	2.52e-02
chr2	152754173	152765037	ENSMUSG00000009876	0.5011	+	Cox4i2	protein_coding	5.31e-07	7.29e-05	3.786596	2.32e+00	2.84e-03	6.06e+00	4.82e-02	2.32e+00	2.32e+00	2.84e-03	2.84e-03	5.70e+00	6.43e+00	3.62e-02	6.03e-02
chr11	34314822	34422640	ENSMUSG00000069911	0.4517	+	Fam196b	protein_coding	5.66e-07	7.68e-05	2.519636	4.98e+00	7.77e-03	7.52e+00	4.58e-02	4.75e+00	5.21e+00	6.52e-03	9.02e-03	7.45e+00	7.58e+00	4.36e-02	4.79e-02
chr10	38965515	39110188	ENSMUSG00000019846	0.4082	+	Lama4	protein_coding	5.97e-07	7.99e-05	3.006664	3.75e+00	1.63e-03	6.69e+00	1.40e-02	3.58e+00	3.91e+00	1.43e-03	1.82e-03	6.25e+00	7.14e+00	9.77e-03	1.82e-02
chr15	102445367	102454630	ENSMUSG00000023047	0.5081	+	Amhr2	protein_coding	6.02e-07	7.99e-05	3.807355	2.00e+00	1.52e-03	6.13e+00	2.62e-02	1.00e+00	3.00e+00	3.80e-04	2.66e-03	6.17e+00	6.09e+00	2.70e-02	2.55e-02
chr7	107595207	107748583	ENSMUSG00000036528	0.4500	+	Ppfibp2	protein_coding	6.93e-07	8.96e-05	2.040146	7.96e+00	3.24e-02	1.00e+01	1.34e-01	8.18e+00	7.74e+00	3.73e-02	2.74e-02	1.01e+01	9.91e+00	1.43e-01	1.24e-01
chr11	31965592	31979651	ENSMUSG00000020299	0.3929	+	4930524B15Rik	protein_coding	6.95e-07	8.96e-05	-5.321928	5.31e+00	1.06e-02	0.00e+00	0.00e+00	5.09e+00	5.52e+00	8.95e-03	1.22e-02	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr13	60934155	60936625	ENSMUSG00000044258	0.4278	-	Ctla2a	protein_coding	6.98e-07	8.96e-05	2.069674	7.17e+00	6.75e-02	9.22e+00	2.85e-01	7.27e+00	7.07e+00	7.22e-02	6.28e-02	9.46e+00	8.98e+00	3.31e-01	2.38e-01
chr7	19778881	19796809	ENSMUSG00000040525	0.5098	-	Cblc	protein_coding	7.60e-07	9.65e-05	-2.763300	6.97e+00	1.71e-02	4.22e+00	2.40e-03	7.30e+00	6.64e+00	2.09e-02	1.32e-02	4.52e+00	3.91e+00	2.93e-03	1.87e-03
chr14	49681560	49783383	ENSMUSG00000036242	0.4023	-	3632451O06Rik	protein_coding	8.86e-07	1.11e-04	3.537965	2.70e+00	1.67e-03	6.22e+00	2.26e-02	2.81e+00	2.58e+00	1.82e-03	1.52e-03	6.02e+00	6.43e+00	1.94e-02	2.58e-02
chr2	180099465	180104488	ENSMUSG00000039059	0.5999	-	Hrh3	protein_coding	9.03e-07	1.12e-04	-2.160177	8.19e+00	6.79e-02	6.04e+00	1.50e-02	8.42e+00	7.95e+00	7.87e-02	5.70e-02	6.19e+00	5.88e+00	1.66e-02	1.34e-02
chr3	96246685	96248504	ENSMUSG00000093769	0.4885	+	Hist2h3c1	protein_coding	9.38e-07	1.15e-04	-3.033015	6.75e+00	4.96e-02	3.75e+00	5.66e-03	6.43e+00	7.08e+00	3.85e-02	6.07e-02	3.81e+00	3.70e+00	5.89e-03	5.44e-03
chr7	96171246	96911093	ENSMUSG00000048078	0.4550	+	Tenm4	protein_coding	9.90e-07	1.21e-04	1.957675	7.19e+00	9.18e-03	9.15e+00	3.58e-02	7.08e+00	7.31e+00	8.42e-03	9.93e-03	8.97e+00	9.32e+00	3.14e-02	4.02e-02
chr11	120486339	120488582	ENSMUSG00000097658	0.5134	-	Gm16755	lincRNA	1.02e-06	1.23e-04	-2.594549	7.24e+00	1.20e-01	4.63e+00	1.91e-02	7.29e+00	7.19e+00	1.24e-01	1.16e-01	4.86e+00	4.39e+00	2.23e-02	1.59e-02
chr3	96245554	96248509	ENSMUSG00000070392	0.5477	+	Gm20634	processed_transcript	1.03e-06	1.23e-04	-3.148863	6.63e+00	8.05e-02	3.52e+00	8.37e-03	6.29e+00	6.98e+00	6.14e-02	9.97e-02	3.58e+00	3.46e+00	8.77e-03	7.97e-03
chr1	156424525	156474331	ENSMUSG00000026600	0.4355	-	Soat1	protein_coding	1.12e-06	1.32e-04	3.440573	2.79e+00	5.09e-04	6.22e+00	6.37e-03	2.58e+00	3.00e+00	4.24e-04	5.94e-04	5.91e+00	6.52e+00	5.01e-03	7.72e-03
chr1	180976210	181020904	ENSMUSG00000038776	0.4875	-	Ephx1	protein_coding	1.46e-06	1.70e-04	2.451211	4.85e+00	7.78e-03	7.45e+00	4.36e-02	4.17e+00	5.52e+00	4.26e-03	1.13e-02	7.52e+00	7.38e+00	4.56e-02	4.16e-02
chr8	54077606	54187096	ENSMUSG00000031520	0.3722	+	Vegfc	protein_coding	1.47e-06	1.71e-04	2.110306	6.01e+00	1.33e-02	8.11e+00	5.79e-02	6.11e+00	5.91e+00	1.42e-02	1.23e-02	7.95e+00	8.28e+00	5.14e-02	6.45e-02
chr1	155844964	155973255	ENSMUSG00000033671	0.4031	-	Cep350	protein_coding	1.57e-06	1.80e-04	-2.074962	8.29e+00	3.19e-02	6.22e+00	7.50e-03	8.46e+00	8.12e+00	3.57e-02	2.82e-02	6.38e+00	6.07e+00	8.31e-03	6.69e-03
chr11	32000463	32059202	ENSMUSG00000020297	0.4637	+	Nsg2	protein_coding	1.58e-06	1.80e-04	1.929861	8.34e+00	4.55e-02	1.03e+01	1.74e-01	8.31e+00	8.37e+00	4.46e-02	4.65e-02	1.01e+01	1.04e+01	1.58e-01	1.90e-01
chr3	43764236	43764764	ENSMUSG00000104083	0.5463	-	Gm29739	processed_pseudogene	1.94e-06	2.18e-04	2.181750	5.60e+00	4.23e-02	7.94e+00	1.95e-01	4.91e+00	6.29e+00	2.31e-02	6.14e-02	7.95e+00	7.92e+00	1.97e-01	1.92e-01
chr5	43993620	44102032	ENSMUSG00000029086	0.4440	-	Prom1	protein_coding	2.14e-06	2.39e-04	2.302563	5.23e+00	2.47e-03	7.50e+00	1.25e-02	5.32e+00	5.13e+00	2.64e-03	2.30e-03	7.22e+00	7.79e+00	1.00e-02	1.49e-02
chrX	8962134	8962975	ENSMUSG00000078208	0.5261	-	Fthl17b	protein_coding	2.27e-06	2.51e-04	-4.507795	5.51e+00	3.82e-02	7.92e-01	8.59e-04	5.55e+00	5.46e+00	3.95e-02	3.69e-02	1.58e+00	0.00e+00	1.72e-03	0.00e+00
chr11	120948480	120960868	ENSMUSG00000025161	0.5604	+	Slc16a3	protein_coding	2.33e-06	2.55e-04	2.273005	1.08e+01	1.96e-01	1.30e+01	9.49e-01	1.05e+01	1.10e+01	1.63e-01	2.29e-01	1.26e+01	1.34e+01	6.83e-01	1.21e+00
chr12	109541001	109571726	ENSMUSG00000021268	0.5022	+	Meg3	lincRNA	2.51e-06	2.72e-04	2.420843	4.95e+00	2.99e-03	7.37e+00	1.64e-02	5.04e+00	4.86e+00	3.19e-03	2.79e-03	7.30e+00	7.44e+00	1.56e-02	1.72e-02
chr2	3341982	3397210	ENSMUSG00000026645	0.4237	-	Olah	protein_coding	2.60e-06	2.78e-04	-5.209453	5.13e+00	2.14e-02	0.00e+00	0.00e+00	4.64e+00	5.61e+00	1.43e-02	2.85e-02	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr5	129846986	129864050	ENSMUSG00000025538	0.4770	+	Sumf2	protein_coding	2.61e-06	2.78e-04	-1.909197	8.86e+00	6.42e-02	6.95e+00	1.70e-02	9.06e+00	8.66e+00	7.32e-02	5.52e-02	7.15e+00	6.75e+00	1.93e-02	1.47e-02
chr6	116199408	116207993	ENSMUSG00000108030	0.4166	-	9530062K07Rik	bidirectional_promoter_lncRNA	2.74e-06	2.90e-04	-3.040414	6.50e+00	7.14e-02	3.46e+00	7.97e-03	6.58e+00	6.41e+00	7.58e-02	6.70e-02	3.46e+00	3.46e+00	7.97e-03	7.97e-03
chr6	122727809	122801640	ENSMUSG00000003153	0.4539	-	Slc2a3	protein_coding	3.31e-06	3.47e-04	2.049770	9.50e+00	5.45e-02	1.16e+01	2.26e-01	9.60e+00	9.41e+00	5.81e-02	5.08e-02	1.16e+01	1.16e+01	2.26e-01	2.26e-01
chr2	84998583	85037462	ENSMUSG00000027071	0.4700	-	P2rx3	protein_coding	4.27e-06	4.43e-04	-3.046294	6.54e+00	2.24e-02	3.45e+00	2.50e-03	6.23e+00	6.85e+00	1.77e-02	2.72e-02	3.00e+00	3.91e+00	1.67e-03	3.34e-03
chr6	81864244	81875864	ENSMUSG00000107817	0.4321	-	Gm38840	unprocessed_pseudogene	4.85e-06	4.99e-04	-4.718818	5.30e+00	3.07e-02	5.00e-01	3.99e-04	5.17e+00	5.43e+00	2.79e-02	3.35e-02	1.00e+00	0.00e+00	7.97e-04	0.00e+00
chr6	40471352	40484700	ENSMUSG00000029911	0.3942	+	Ssbp1	protein_coding	5.05e-06	5.15e-04	-1.883561	1.04e+01	1.25e-01	8.49e+00	3.38e-02	1.05e+01	1.02e+01	1.40e-01	1.10e-01	8.44e+00	8.54e+00	3.27e-02	3.50e-02
chr10	81590904	81601073	ENSMUSG00000034758	0.5438	-	Tle6	protein_coding	5.12e-06	5.18e-04	2.758445	3.73e+00	2.06e-03	6.66e+00	1.48e-02	3.00e+00	4.46e+00	1.03e-03	3.10e-03	6.55e+00	6.77e+00	1.37e-02	1.59e-02
chr6	115954811	115995005	ENSMUSG00000030123	0.5612	-	Plxnd1	protein_coding	5.17e-06	5.18e-04	1.969194	6.13e+00	1.47e-02	8.10e+00	5.83e-02	5.86e+00	6.39e+00	1.20e-02	1.75e-02	7.89e+00	8.32e+00	4.97e-02	6.69e-02
chr7	109138572	109175207	ENSMUSG00000036111	0.5109	-	Lmo1	protein_coding	5.25e-06	5.22e-04	1.912306	6.58e+00	6.21e-02	8.52e+00	2.36e-01	6.30e+00	6.86e+00	5.02e-02	7.41e-02	8.46e+00	8.58e+00	2.26e-01	2.45e-01
chr7	142892752	142931128	ENSMUSG00000000214	0.5159	-	Th	protein_coding	5.80e-06	5.68e-04	1.859402	7.56e+00	1.46e-02	9.44e+00	5.31e-02	7.84e+00	7.29e+00	1.74e-02	1.18e-02	9.61e+00	9.27e+00	5.93e-02	4.69e-02
chrX	9043727	9044242	ENSMUSG00000083714	0.5465	+	Fthl17-ps1	unprocessed_pseudogene	5.81e-06	5.68e-04	-4.409391	5.39e+00	3.31e-02	7.92e-01	7.97e-04	5.13e+00	5.64e+00	2.71e-02	3.91e-02	1.58e+00	0.00e+00	1.59e-03	0.00e+00
chr11	54100095	54131665	ENSMUSG00000018906	0.4982	+	P4ha2	protein_coding	6.18e-06	5.99e-04	1.816203	8.29e+00	2.22e-02	1.01e+01	7.85e-02	8.28e+00	8.31e+00	2.20e-02	2.25e-02	1.00e+01	1.02e+01	7.53e-02	8.17e-02
chr8	25039144	25102376	ENSMUSG00000031557	0.4692	-	Plekha2	protein_coding	6.40e-06	6.11e-04	3.879146	1.66e+00	2.92e-04	5.69e+00	5.91e-03	1.00e+00	2.32e+00	1.17e-04	4.68e-04	5.64e+00	5.73e+00	5.73e-03	6.08e-03
chr11	3649494	3690477	ENSMUSG00000034543	0.4056	+	Morc2a	protein_coding	6.40e-06	6.11e-04	-1.789114	9.05e+00	8.01e-02	7.22e+00	2.31e-02	8.93e+00	9.16e+00	7.38e-02	8.64e-02	7.55e+00	6.89e+00	2.83e-02	1.79e-02
chr3	81036416	81214040	ENSMUSG00000028019	0.3820	+	Pdgfc	protein_coding	6.63e-06	6.26e-04	4.700440	5.00e-01	4.81e-05	5.28e+00	3.65e-03	1.00e+00	0.00e+00	9.62e-05	0.00e+00	5.13e+00	5.43e+00	3.27e-03	4.04e-03
chr3	55242364	55539068	ENSMUSG00000027797	0.4253	+	Dclk1	protein_coding	6.66e-06	6.26e-04	1.937499	6.41e+00	4.11e-03	8.35e+00	1.59e-02	6.30e+00	6.52e+00	3.79e-03	4.42e-03	8.39e+00	8.32e+00	1.63e-02	1.55e-02
chr3	155084918	155093403	ENSMUSG00000053870	0.3725	-	Fpgt	protein_coding	6.73e-06	6.28e-04	-2.411631	7.04e+00	1.74e-02	4.73e+00	3.17e-03	7.58e+00	6.51e+00	2.36e-02	1.12e-02	4.81e+00	4.64e+00	3.35e-03	2.98e-03
chr2	3114224	3227806	ENSMUSG00000050530	0.4474	+	Fam171a1	protein_coding	6.83e-06	6.29e-04	1.952171	6.10e+00	1.61e-02	8.04e+00	6.29e-02	6.25e+00	5.95e+00	1.77e-02	1.44e-02	7.81e+00	8.28e+00	5.27e-02	7.31e-02
chr18	31437808	31447415	ENSMUSG00000024261	0.3748	-	Syt4	protein_coding	6.85e-06	6.29e-04	1.844259	8.98e+00	1.39e-01	1.08e+01	4.99e-01	8.88e+00	9.07e+00	1.30e-01	1.48e-01	1.06e+01	1.10e+01	4.39e-01	5.59e-01
chr11	35808381	35834506	ENSMUSG00000018848	0.4378	-	Rars	protein_coding	7.03e-06	6.41e-04	-1.887494	1.12e+01	3.61e-01	9.26e+00	9.74e-02	1.13e+01	1.11e+01	3.84e-01	3.38e-01	9.51e+00	9.01e+00	1.14e-01	8.05e-02
chr3	103058285	103067914	ENSMUSG00000027852	0.4200	+	Nras	protein_coding	7.10e-06	6.42e-04	2.074158	1.05e+01	1.20e-01	1.26e+01	5.05e-01	1.06e+01	1.05e+01	1.22e-01	1.18e-01	1.22e+01	1.29e+01	3.80e-01	6.31e-01
chr19	8836929	8839246	ENSMUSG00000071658	0.5198	-	Gng3	protein_coding	7.42e-06	6.66e-04	1.908751	6.15e+00	3.10e-02	8.17e+00	1.18e-01	5.52e+00	6.77e+00	1.82e-02	4.38e-02	7.97e+00	8.37e+00	1.01e-01	1.34e-01
chr14	27238039	27403904	ENSMUSG00000021895	0.4456	+	Arhgef3	protein_coding	7.54e-06	6.68e-04	2.480626	4.43e+00	6.45e-03	6.87e+00	3.74e-02	4.39e+00	4.46e+00	6.29e-03	6.61e-03	6.57e+00	7.18e+00	2.96e-02	4.53e-02
chr15	5095981	5108539	ENSMUSG00000041849	0.4326	-	Card6	protein_coding	7.55e-06	6.68e-04	-3.280108	6.09e+00	1.36e-02	2.79e+00	1.22e-03	6.07e+00	6.11e+00	1.34e-02	1.38e-02	3.00e+00	2.58e+00	1.43e-03	1.02e-03
chr2	28192992	28230736	ENSMUSG00000026833	0.5231	+	Olfm1	protein_coding	7.71e-06	6.76e-04	1.828264	8.68e+00	1.07e-01	1.05e+01	3.80e-01	8.74e+00	8.63e+00	1.11e-01	1.03e-01	1.05e+01	1.06e+01	3.69e-01	3.91e-01
chr7	127244274	127254803	ENSMUSG00000054716	0.5180	+	Zfp771	protein_coding	7.95e-06	6.93e-04	1.859137	6.73e+00	2.70e-02	8.61e+00	9.85e-02	6.49e+00	6.97e+00	2.25e-02	3.14e-02	8.60e+00	8.61e+00	9.80e-02	9.90e-02
chr15	66929321	66969640	ENSMUSG00000005125	0.4895	-	Ndrg1	protein_coding	8.23e-06	7.12e-04	4.185867	1.29e+00	1.20e-04	5.50e+00	3.55e-03	1.58e+00	1.00e+00	1.60e-04	7.98e-05	5.61e+00	5.39e+00	3.83e-03	3.27e-03
chr9	17030045	17030896	ENSMUSG00000090602	0.4237	+	Gm5611	processed_pseudogene	8.75e-06	7.50e-04	-2.408049	1.34e+01	1.59e+01	1.19e+01	2.99e+00	1.52e+01	1.17e+01	2.92e+01	2.56e+00	1.20e+01	1.17e+01	3.33e+00	2.65e+00
chr19	4510472	4621752	ENSMUSG00000024892	0.4622	+	Pcx	protein_coding	8.80e-06	7.50e-04	1.983126	5.98e+00	3.70e-02	7.99e+00	1.48e-01	5.67e+00	6.29e+00	2.91e-02	4.48e-02	7.94e+00	8.05e+00	1.42e-01	1.54e-01
chr17	37164373	37164969	ENSMUSG00000092408	0.5528	-	Vhl-ps1	processed_pseudogene	9.13e-06	7.73e-04	5.000000	0.00e+00	0.00e+00	2.99e+00	2.47e-02	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	5.98e+00	0.00e+00	4.94e-02
chr17	33843091	33849774	ENSMUSG00000002308	0.4984	+	Cd320	protein_coding	1.00e-05	8.37e-04	2.091922	5.57e+00	1.21e-02	7.66e+00	5.23e-02	5.64e+00	5.49e+00	1.27e-02	1.14e-02	7.55e+00	7.77e+00	4.83e-02	5.63e-02
chr10	76057494	76110961	ENSMUSG00000020226	0.4225	-	Slc5a4b	protein_coding	1.00e-05	8.37e-04	-2.111893	7.64e+00	8.17e-02	5.54e+00	1.86e-02	7.81e+00	7.48e+00	9.11e-02	7.23e-02	5.64e+00	5.43e+00	2.00e-02	1.72e-02
chr14	42463881	42472122	ENSMUSG00000094954	0.4038	+	Gm8011	protein_coding	1.02e-05	8.46e-04	3.258734	2.79e+00	1.77e-03	6.06e+00	1.95e-02	2.58e+00	3.00e+00	1.48e-03	2.07e-03	6.00e+00	6.13e+00	1.86e-02	2.04e-02
chr13	34074301	34078008	ENSMUSG00000058672	0.5224	-	Tubb2a	protein_coding	1.05e-05	8.65e-04	-1.843547	1.07e+01	1.21e+00	8.90e+00	3.36e-01	1.08e+01	1.07e+01	1.24e+00	1.18e+00	8.91e+00	8.89e+00	3.37e-01	3.34e-01
chr19	57032733	57314919	ENSMUSG00000025085	0.4576	-	Ablim1	protein_coding	1.18e-05	9.64e-04	1.773540	7.21e+00	9.94e-03	8.98e+00	3.41e-02	7.18e+00	7.24e+00	9.74e-03	1.01e-02	8.92e+00	9.04e+00	3.28e-02	3.55e-02
chr6	47530139	47595341	ENSMUSG00000029687	0.3995	-	Ezh2	protein_coding	1.20e-05	9.64e-04	1.690034	9.07e+00	2.88e-02	1.05e+01	9.31e-02	9.63e+00	8.50e+00	3.96e-02	1.81e-02	9.50e+00	1.16e+01	3.62e-02	1.50e-01
chr13	4268172	4279433	ENSMUSG00000021211	0.3718	-	Akr1c12	protein_coding	1.20e-05	9.64e-04	-1.918729	8.39e+00	2.28e-01	6.47e+00	5.99e-02	8.38e+00	8.41e+00	2.26e-01	2.31e-01	6.30e+00	6.63e+00	5.31e-02	6.67e-02
chr8	22644861	22646286	ENSMUSG00000097839	0.3731	+	Gm26909	lincRNA	1.21e-05	9.69e-04	-2.202556	7.28e+00	1.24e-01	5.05e+00	2.63e-02	7.10e+00	7.46e+00	1.08e-01	1.40e-01	5.39e+00	4.70e+00	3.27e-02	1.99e-02
chr5	137857045	137860318	ENSMUSG00000106478	0.4994	+	Gm36551	antisense	1.27e-05	1.01e-03	-3.226068	6.03e+00	5.14e-02	2.75e+00	4.78e-03	6.17e+00	5.88e+00	5.66e-02	4.63e-02	3.17e+00	2.32e+00	6.38e-03	3.19e-03
chr1	58702309	58703986	ENSMUSG00000101046	0.4309	+	Gm28803	processed_pseudogene	1.27e-05	1.01e-03	-5.044394	4.88e+00	2.55e-02	0.00e+00	0.00e+00	4.17e+00	5.58e+00	1.36e-02	3.75e-02	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr4	138565360	138635884	ENSMUSG00000028753	0.4947	-	Vwa5b1	protein_coding	1.37e-05	1.07e-03	1.948846	5.92e+00	2.75e-03	7.87e+00	1.08e-02	5.78e+00	6.07e+00	2.48e-03	3.03e-03	7.72e+00	8.02e+00	9.64e-03	1.19e-02
chr4	154020470	154026230	ENSMUSG00000078350	0.5721	-	Smim1	protein_coding	1.39e-05	1.08e-03	1.932402	6.09e+00	1.21e-02	8.08e+00	4.69e-02	5.67e+00	6.51e+00	8.68e-03	1.56e-02	8.21e+00	7.94e+00	5.12e-02	4.25e-02
chr12	85166639	85177296	ENSMUSG00000004791	0.5403	-	Pgf	protein_coding	1.40e-05	1.09e-03	1.953146	6.05e+00	3.23e-02	8.01e+00	1.27e-01	6.19e+00	5.91e+00	3.55e-02	2.91e-02	8.08e+00	7.93e+00	1.33e-01	1.20e-01
chr15	89429589	89430868	ENSMUSG00000079242	0.5109	+	C730034F03Rik	protein_coding	1.50e-05	1.16e-03	-2.722466	6.61e+00	5.07e-02	3.89e+00	7.24e-03	6.87e+00	6.34e+00	6.00e-02	4.14e-02	3.70e+00	4.09e+00	6.21e-03	8.28e-03
chr14	51905271	51913488	ENSMUSG00000004558	0.5169	-	Ndrg2	protein_coding	1.55e-05	1.19e-03	1.895811	5.91e+00	2.83e-02	7.76e+00	1.06e-01	5.70e+00	6.13e+00	2.40e-02	3.25e-02	7.33e+00	8.19e+00	7.54e-02	1.38e-01
chr7	127434422	127439392	ENSMUSG00000108483	0.4669	+	Gm45184	lincRNA	1.57e-05	1.19e-03	-2.475338	6.83e+00	9.01e-02	4.35e+00	1.56e-02	6.85e+00	6.82e+00	9.09e-02	8.93e-02	4.46e+00	4.25e+00	1.67e-02	1.44e-02
chrX	153723590	153741920	ENSMUSG00000086316	0.3856	+	2210013O21Rik	protein_coding	1.60e-05	1.21e-03	1.794755	6.84e+00	1.71e-02	8.64e+00	5.99e-02	6.74e+00	6.94e+00	1.59e-02	1.83e-02	8.69e+00	8.59e+00	6.18e-02	5.79e-02
chr2	157418444	157471862	ENSMUSG00000027646	0.5349	+	Src	protein_coding	1.70e-05	1.28e-03	2.456065	4.23e+00	3.21e-03	6.70e+00	1.84e-02	3.81e+00	4.64e+00	2.26e-03	4.16e-03	6.34e+00	7.06e+00	1.39e-02	2.29e-02
chr8	45336717	45382291	ENSMUSG00000070044	0.4243	-	Fam149a	protein_coding	2.02e-05	1.50e-03	3.398549	2.40e+00	1.17e-03	5.85e+00	1.49e-02	2.00e+00	2.81e+00	7.82e-04	1.56e-03	5.98e+00	5.73e+00	1.62e-02	1.36e-02
chr7	82173840	82307419	ENSMUSG00000030638	0.4685	+	Sh3gl3	protein_coding	2.04e-05	1.51e-03	2.250375	4.93e+00	2.92e-03	7.20e+00	1.43e-02	4.64e+00	5.21e+00	2.34e-03	3.50e-03	7.32e+00	7.08e+00	1.55e-02	1.30e-02
chr12	111417430	111431182	ENSMUSG00000021280	0.5504	+	Exoc3l4	protein_coding	2.10e-05	1.54e-03	3.157541	2.23e+00	3.47e-03	5.60e+00	3.60e-02	3.46e+00	1.00e+00	6.31e-03	6.31e-04	4.70e+00	6.49e+00	1.58e-02	5.62e-02
chr5	90460897	90476602	ENSMUSG00000029368	0.3900	+	Alb	protein_coding	2.15e-05	1.57e-03	-5.000000	2.99e+00	6.29e-03	0.00e+00	0.00e+00	0.00e+00	5.98e+00	0.00e+00	1.26e-02	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr14	7817957	7951588	ENSMUSG00000025278	0.4609	+	Flnb	protein_coding	2.21e-05	1.61e-03	-1.689741	9.74e+00	3.64e-01	8.05e+00	1.13e-01	9.86e+00	9.61e+00	3.96e-01	3.32e-01	8.14e+00	7.96e+00	1.20e-01	1.06e-01
chr19	8837467	8848683	ENSMUSG00000071657	0.4953	+	Bscl2	protein_coding	2.32e-05	1.68e-03	1.720012	8.74e+00	4.63e-02	1.05e+01	1.53e-01	8.64e+00	8.84e+00	4.31e-02	4.94e-02	1.04e+01	1.05e+01	1.48e-01	1.57e-01
chr7	100937630	100974649	ENSMUSG00000048779	0.5024	-	P2ry6	protein_coding	2.42e-05	1.74e-03	4.321928	7.92e-01	2.33e-04	5.23e+00	9.08e-03	0.00e+00	1.58e+00	0.00e+00	4.66e-04	5.75e+00	4.70e+00	1.23e-02	5.82e-03
chr7	143688762	143756985	ENSMUSG00000037606	0.5142	-	Osbpl5	protein_coding	2.51e-05	1.80e-03	1.845992	6.64e+00	1.35e-02	8.44e+00	4.89e-02	6.67e+00	6.61e+00	1.38e-02	1.32e-02	8.82e+00	8.06e+00	6.16e-02	3.63e-02
chr18	41875696	41951194	ENSMUSG00000056671	0.4234	-	Prelid2	protein_coding	2.70e-05	1.92e-03	1.777901	6.58e+00	4.19e-02	8.46e+00	1.45e-01	7.12e+00	6.04e+00	5.69e-02	2.68e-02	8.44e+00	8.48e+00	1.43e-01	1.46e-01
chr9	44686304	44735198	ENSMUSG00000048537	0.5320	-	Phldb1	protein_coding	2.78e-05	1.96e-03	1.790676	6.43e+00	5.76e-03	8.23e+00	2.01e-02	6.25e+00	6.61e+00	5.03e-03	6.50e-03	8.19e+00	8.27e+00	1.96e-02	2.06e-02
chr11	106300271	106301865	ENSMUSG00000020713	0.5643	-	Gh	protein_coding	2.79e-05	1.96e-03	4.977280	0.00e+00	0.00e+00	4.96e+00	2.26e-02	0.00e+00	0.00e+00	0.00e+00	0.00e+00	5.17e+00	4.75e+00	2.59e-02	1.93e-02
chr12	24651371	24662774	ENSMUSG00000020653	0.4784	+	Klf11	protein_coding	3.08e-05	2.15e-03	-1.678569	9.17e+00	9.54e-02	7.50e+00	2.97e-02	9.34e+00	9.01e+00	1.06e-01	8.44e-02	7.52e+00	7.48e+00	3.01e-02	2.93e-02
chr5	74592447	74702912	ENSMUSG00000029228	0.4471	-	Lnx1	protein_coding	3.12e-05	2.17e-03	3.488286	2.16e+00	2.25e-04	5.66e+00	3.18e-03	2.32e+00	2.00e+00	2.57e-04	1.93e-04	5.61e+00	5.70e+00	3.09e-03	3.28e-03
chr13	48535523	48537822	ENSMUSG00000097375	0.3513	-	6720427I07Rik	lincRNA	3.16e-05	2.18e-03	-1.667606	8.86e+00	3.72e-01	7.18e+00	1.16e-01	8.71e+00	9.01e+00	3.33e-01	4.10e-01	7.40e+00	6.97e+00	1.34e-01	9.89e-02
chr8	33782643	33929863	ENSMUSG00000031586	0.4657	-	Rbpms	protein_coding	3.30e-05	2.27e-03	2.430351	4.25e+00	1.97e-03	6.78e+00	1.11e-02	3.70e+00	4.81e+00	1.21e-03	2.73e-03	6.95e+00	6.60e+00	1.24e-02	9.70e-03
chr7	66390890	66427517	ENSMUSG00000015134	0.4848	-	Aldh1a3	protein_coding	3.41e-05	2.31e-03	-1.708819	1.11e+01	3.14e-01	9.42e+00	9.58e-02	1.14e+01	1.09e+01	3.75e-01	2.52e-01	9.77e+00	9.07e+00	1.19e-01	7.31e-02
chr7	112116017	112159057	ENSMUSG00000030772	0.4891	-	Dkk3	protein_coding	3.41e-05	2.31e-03	1.650187	7.58e+00	5.51e-02	9.23e+00	1.74e-01	7.52e+00	7.63e+00	5.31e-02	5.71e-02	9.16e+00	9.29e+00	1.65e-01	1.82e-01
chr11	70017085	70045532	ENSMUSG00000020886	0.5237	+	Dlg4	protein_coding	3.48e-05	2.35e-03	1.628374	7.48e+00	2.14e-02	9.16e+00	6.63e-02	7.06e+00	7.89e+00	1.53e-02	2.74e-02	9.01e+00	9.30e+00	5.97e-02	7.28e-02
chr7	74275419	74554780	ENSMUSG00000025790	0.4548	-	Slco3a1	protein_coding	3.59e-05	2.41e-03	1.693204	9.05e+00	3.85e-02	1.07e+01	1.25e-01	9.01e+00	9.09e+00	3.74e-02	3.97e-02	1.06e+01	1.08e+01	1.16e-01	1.33e-01
chrX	142681406	142843494	ENSMUSG00000047045	0.4337	+	Tmem164	protein_coding	3.61e-05	2.41e-03	1.658063	7.90e+00	2.45e-02	9.57e+00	7.77e-02	7.77e+00	8.03e+00	2.23e-02	2.68e-02	9.63e+00	9.50e+00	8.12e-02	7.42e-02
chr13	96388294	96415587	ENSMUSG00000021671	0.4204	+	Poc5	protein_coding	3.71e-05	2.45e-03	-1.636844	8.68e+00	1.84e-01	7.00e+00	5.90e-02	8.79e+00	8.56e+00	1.99e-01	1.69e-01	7.37e+00	6.63e+00	7.38e-02	4.41e-02
chr17	33915899	33929288	ENSMUSG00000024308	0.5340	+	Tapbp	protein_coding	3.72e-05	2.45e-03	1.756130	6.42e+00	1.72e-02	8.17e+00	5.86e-02	6.54e+00	6.30e+00	1.86e-02	1.58e-02	7.97e+00	8.37e+00	5.06e-02	6.66e-02
chr4	107968332	108013532	ENSMUSG00000008932	0.5038	+	Slc1a7	protein_coding	3.95e-05	2.59e-03	-2.156276	7.03e+00	5.58e-02	4.83e+00	1.22e-02	7.20e+00	6.86e+00	6.25e-02	4.92e-02	5.21e+00	4.46e+00	1.54e-02	8.99e-03
chr3	96239749	96240276	ENSMUSG00000063954	0.6420	-	Hist2h2aa2	protein_coding	3.99e-05	2.60e-03	-2.733826	6.25e+00	6.81e-02	3.51e+00	9.48e-03	6.41e+00	6.09e+00	7.58e-02	6.05e-02	3.70e+00	3.32e+00	1.08e-02	8.12e-03
chr3	96269721	96279001	ENSMUSG00000050936	0.4661	+	Gm42743	lincRNA	4.08e-05	2.65e-03	-3.093109	5.99e+00	5.02e-02	2.90e+00	5.18e-03	6.17e+00	5.81e+00	5.66e-02	4.39e-02	3.00e+00	2.81e+00	5.58e-03	4.78e-03
chr17	35379617	35385290	ENSMUSG00000035929	0.5178	+	H2-Q4	protein_coding	4.26e-05	2.74e-03	2.965235	3.35e+00	2.26e-03	5.83e+00	1.92e-02	3.00e+00	3.70e+00	1.66e-03	2.85e-03	7.14e+00	4.52e+00	3.33e-02	5.23e-03
chr9	69453620	69491795	ENSMUSG00000032231	0.4734	+	Anxa2	protein_coding	4.29e-05	2.74e-03	1.569143	8.45e+00	6.61e-02	9.97e+00	1.96e-01	8.37e+00	8.54e+00	6.22e-02	6.99e-02	9.58e+00	1.04e+01	1.45e-01	2.48e-01

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

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.