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 (27212 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 (3269 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 0.04226 average reads per 100 bp), iii) genes with read counts below the median read counts of the total normalized count distribution (9912 genes with cutoff value 10 normalized read counts). The total number of genes excluded due to the application of gene filters was 4961. The total (unified) number of genes excluded due to the application of all filters was 38147. The resulting gene counts table was subjected to differential expression analysis for the contrasts Lbil22 versus Lcil22 using the Bioconductor package DESeq. The final numbers of differentially expressed genes were (per contrast): for the contrast Lbil22 versus Lcil22, 1185 (223) statistically significant genes were found with a p-value (FDR or adjusted p-value) threshold of 0.05 and of these, 306 (135) were up-regulated, 248 (79) were down-regulated and 631 (9) 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: Lbil22, Lcil22
Samples included: SK3R16_Lbil22_7, SK3R17_Lbil22_8, SK3R18_Lbil22_9, SK3R22_Lcil22_8, SK3R23_Lcil22_11, SK3R24_Lcil22_12
Samples excluded: none
Requested contrasts: Lbil22_vs_Lcil22
Library sizes: not available
Annotation: embedded
Organism:
Annotation source: Ensembl genomes
Count type: utr
Exon filters: none applied
Gene filters: length, avg.reads, expression, biotype

length
- length: 500

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

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

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

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

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

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

method: blind
sharingMode: fit-only
fitType: local

Meta-analysis method: no meta-analysis, reported p-values from the first supplied statistical algorithm
Multiple testing correction: Benjamini-Hochberg FDR
p-value threshold: 0.05
Logarithmic tranformation offset: 1
Analysis preset: not available
Quality control plots: multidimensional scaling, biotype detection, biotype counts, sample and biotype saturation, filtered biotypes, correlation heatmap and correlogram, pairwise scatterplots between samples, boxplots, GC-content bias, transcript length bias, DEG biotype detection, volcano plot, DEG heatmap
Figure format: png, pdf
Output directory: /data/images/proton2/run374/SKobold/metaseqr_quantseq_Lbil22VSLcil22
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 56 seconds

Filtered genes

Number of filtered genes: 38147 which is the union of

Filtered because of zero reads: 27212
Filtered because of exon filters: 0
Filtered because of gene filters: 10935 which is the union of
- length: 3269 genes with filter cutoff value 500
- avg.reads: 1643 genes with filter cutoff value 0.0422565
- expression: 9912 genes further decomposed to (filter name, filtered genes, filter cutoff):
- - median: 9912 genes with filter cutoff value 10
- biotype: 48 genes with filter cutoff value rRNA, IG_V_pseudogene, TR_V_pseudogene

Differentially expressed genes

Number of differentially expressed genes per contrast:

Lbil22_vs_Lcil22: 1185 (223) statistically significant genes of which 306 (135) up regulated, 248 (79) down regulated and 631 (9) not differentially expressed according to a p-value (FDR or adjusted p-value) threshold of 0.05 and an absolute fold change cutoff value of 1 in log₂ scale.

Run log

[ 2017-12-12 16:04:39 ] INFO 2017-12-12 16:04:39: Data processing started...
[ 2017-12-12 16:04:39 ] INFO Read counts file: imported custom data frame
[ 2017-12-12 16:04:39 ] INFO Conditions: Lbil22, Lcil22
[ 2017-12-12 16:04:39 ] INFO Samples to include: SK3R16_Lbil22_7, SK3R17_Lbil22_8, SK3R18_Lbil22_9, SK3R22_Lcil22_8, SK3R23_Lcil22_11, SK3R24_Lcil22_12
[ 2017-12-12 16:04:39 ] INFO Samples to exclude: none
[ 2017-12-12 16:04:39 ] INFO Requested contrasts: Lbil22_vs_Lcil22
[ 2017-12-12 16:04:39 ] INFO Annotation: embedded
[ 2017-12-12 16:04:39 ] INFO Organism: custom
[ 2017-12-12 16:04:39 ] INFO Reference source: ensembl
[ 2017-12-12 16:04:39 ] INFO Count type: utr
[ 2017-12-12 16:04:39 ] INFO Exon filters: none applied
[ 2017-12-12 16:04:39 ] INFO Gene filters: length, avg.reads, expression, biotype
[ 2017-12-12 16:04:39 ] INFO length:
[ 2017-12-12 16:04:39 ] INFO length: 500
[ 2017-12-12 16:04:39 ] INFO avg.reads:
[ 2017-12-12 16:04:39 ] INFO average.per.bp: 100
[ 2017-12-12 16:04:39 ] INFO quantile: 0.25
[ 2017-12-12 16:04:39 ] INFO expression:
[ 2017-12-12 16:04:39 ] INFO median: TRUE
[ 2017-12-12 16:04:39 ] INFO mean: FALSE
[ 2017-12-12 16:04:39 ] INFO quantile: NA
[ 2017-12-12 16:04:39 ] INFO known: NA
[ 2017-12-12 16:04:39 ] INFO custom: NA
[ 2017-12-12 16:04:39 ] INFO biotype:
[ 2017-12-12 16:04:39 ] INFO pseudogene: FALSE
[ 2017-12-12 16:04:39 ] INFO snRNA: FALSE
[ 2017-12-12 16:04:39 ] INFO protein_coding: FALSE
[ 2017-12-12 16:04:39 ] INFO antisense: FALSE
[ 2017-12-12 16:04:39 ] INFO miRNA: FALSE
[ 2017-12-12 16:04:39 ] INFO snoRNA: FALSE
[ 2017-12-12 16:04:39 ] INFO lincRNA: FALSE
[ 2017-12-12 16:04:39 ] INFO processed_transcript: FALSE
[ 2017-12-12 16:04:39 ] INFO misc_RNA: FALSE
[ 2017-12-12 16:04:39 ] INFO rRNA: TRUE
[ 2017-12-12 16:04:39 ] INFO sense_intronic: FALSE
[ 2017-12-12 16:04:39 ] INFO sense_overlapping: FALSE
[ 2017-12-12 16:04:39 ] INFO polymorphic_pseudogene: FALSE
[ 2017-12-12 16:04:39 ] INFO IG_C_gene: FALSE
[ 2017-12-12 16:04:39 ] INFO IG_J_gene: FALSE
[ 2017-12-12 16:04:39 ] INFO IG_D_gene: FALSE
[ 2017-12-12 16:04:39 ] INFO IG_LV_gene: FALSE
[ 2017-12-12 16:04:39 ] INFO IG_V_gene: FALSE
[ 2017-12-12 16:04:39 ] INFO IG_V_pseudogene: TRUE
[ 2017-12-12 16:04:39 ] INFO TR_V_gene: FALSE
[ 2017-12-12 16:04:39 ] INFO TR_V_pseudogene: TRUE
[ 2017-12-12 16:04:39 ] INFO three_prime_overlapping_ncrna: FALSE
[ 2017-12-12 16:04:39 ] INFO Filter application: postnorm
[ 2017-12-12 16:04:39 ] INFO Normalization algorithm: deseq
[ 2017-12-12 16:04:39 ] INFO Normalization arguments:
[ 2017-12-12 16:04:39 ] INFO locfunc:
[ 2017-12-12 16:04:39 ] INFO [[
[ 2017-12-12 16:04:39 ] INFO list(locfunc = function (x, na.rm = FALSE) standardGeneric("median"))
[ 2017-12-12 16:04:39 ] INFO locfunc
[ 2017-12-12 16:04:39 ] INFO Statistical algorithm: deseq
[ 2017-12-12 16:04:39 ] INFO Statistical arguments:
[ 2017-12-12 16:04:39 ] INFO deseq: blind, fit-only, local
[ 2017-12-12 16:04:39 ] INFO Meta-analysis method: none
[ 2017-12-12 16:04:39 ] INFO Multiple testing correction: BH
[ 2017-12-12 16:04:39 ] INFO p-value threshold: 0.05
[ 2017-12-12 16:04:39 ] INFO Logarithmic transformation offset: 1
[ 2017-12-12 16:04:39 ] INFO Quality control plots: mds, biodetection, countsbio, saturation, readnoise, filtered, correl, pairwise, boxplot, gcbias, lengthbias, biodist, volcano, deheatmap
[ 2017-12-12 16:04:39 ] INFO Figure format: png, pdf
[ 2017-12-12 16:04:39 ] INFO Output directory: /data/images/proton2/run374/SKobold/metaseqr_quantseq_Lbil22VSLcil22
[ 2017-12-12 16:04:39 ] INFO Output data: annotation, p.value, adj.p.value, fold.change, stats, counts, flags
[ 2017-12-12 16:04:39 ] INFO Output scale(s): log2, rpgm
[ 2017-12-12 16:04:39 ] INFO Output values: normalized
[ 2017-12-12 16:04:39 ] INFO Output statistics: mean
[ 2017-12-12 16:04:39 ] INFO Reading custom external gene annotation for from /data/results/tools/rnaseq/metaseqr/mm10/gene_data_mm10.txt.gz...
[ 2017-12-12 16:04:41 ] INFO Checking chromosomes in transcript counts and gene annotation...
[ 2017-12-12 16:04:41 ] INFO Processing transcripts...
[ 2017-12-12 16:04:41 ] INFO Separating transcripts (UTR regions) per gene for SK3R16_Lbil22_7...
[ 2017-12-12 16:05:01 ] INFO Separating transcripts (UTR regions) per gene for SK3R17_Lbil22_8...
[ 2017-12-12 16:05:23 ] INFO Separating transcripts (UTR regions) per gene for SK3R18_Lbil22_9...
[ 2017-12-12 16:05:45 ] INFO Separating transcripts (UTR regions) per gene for SK3R22_Lcil22_8...
[ 2017-12-12 16:06:06 ] INFO Separating transcripts (UTR regions) per gene for SK3R23_Lcil22_11...
[ 2017-12-12 16:06:27 ] INFO Separating transcripts (UTR regions) per gene for SK3R24_Lcil22_12...
[ 2017-12-12 16:06:46 ] INFO Saving gene model to /data/images/proton2/run374/SKobold/metaseqr_quantseq_Lbil22VSLcil22/data/gene_model.RData
[ 2017-12-12 16:06:48 ] INFO Summarizing count data...
[ 2017-12-12 16:06:59 ] INFO Removing genes with zero counts in all samples...
[ 2017-12-12 16:06:59 ] INFO Normalizing with: deseq
[ 2017-12-12 16:07:00 ] INFO Applying gene filter length...
[ 2017-12-12 16:07:00 ] INFO Threshold below which ignored: 500
[ 2017-12-12 16:07:00 ] INFO Applying gene filter avg.reads...
[ 2017-12-12 16:07:00 ] INFO Threshold below which ignored: 0.042256502593041
[ 2017-12-12 16:07:00 ] INFO Applying gene filter expression...
[ 2017-12-12 16:07:00 ] INFO Threshold below which ignored: 10
[ 2017-12-12 16:07:00 ] INFO Applying gene filter biotype...
[ 2017-12-12 16:07:00 ] INFO Biotypes ignored: rRNA, IG_V_pseudogene, TR_V_pseudogene
[ 2017-12-12 16:07:00 ] INFO 38147 genes filtered out
[ 2017-12-12 16:07:00 ] INFO 11401 genes remain after filtering
[ 2017-12-12 16:07:00 ] INFO Running statistical tests with: deseq
[ 2017-12-12 16:07:00 ] INFO Contrast: Lbil22_vs_Lcil22
[ 2017-12-12 16:07:08 ] INFO Contrast Lbil22_vs_Lcil22: found 1185 genes
[ 2017-12-12 16:07:08 ] INFO Building output files...
[ 2017-12-12 16:07:11 ] INFO Contrast: Lbil22_vs_Lcil22
[ 2017-12-12 16:07:11 ] INFO Adding non-filtered data...
[ 2017-12-12 16:07:11 ] INFO binding annotation...
[ 2017-12-12 16:07:11 ] INFO binding p-values...
[ 2017-12-12 16:07:11 ] INFO binding FDRs...
[ 2017-12-12 16:07:11 ] INFO binding log2 normalized fold changes...
[ 2017-12-12 16:07:11 ] INFO binding normalized mean counts...
[ 2017-12-12 16:07:12 ] INFO binding normalized mean counts...
[ 2017-12-12 16:07:12 ] INFO binding all normalized counts for Lbil22...
[ 2017-12-12 16:07:12 ] INFO binding all normalized counts for Lcil22...
[ 2017-12-12 16:07:13 ] INFO binding filtering flags...
[ 2017-12-12 16:07:13 ] INFO Writing output...
[ 2017-12-12 16:07:13 ] INFO Adding filtered data...
[ 2017-12-12 16:07:13 ] INFO binding annotation...
[ 2017-12-12 16:07:13 ] INFO binding p-values...
[ 2017-12-12 16:07:13 ] INFO binding FDRs...
[ 2017-12-12 16:07:14 ] INFO binding log2 normalized fold changes...
[ 2017-12-12 16:07:14 ] INFO binding normalized mean counts...
[ 2017-12-12 16:07:14 ] INFO binding normalized mean counts...
[ 2017-12-12 16:07:15 ] INFO binding all normalized counts for Lbil22...
[ 2017-12-12 16:07:15 ] INFO binding all normalized counts for Lcil22...
[ 2017-12-12 16:07:15 ] INFO binding filtering flags...
[ 2017-12-12 16:07:16 ] INFO Writing output...
[ 2017-12-12 16:07:18 ] INFO Creating quality control graphs...
[ 2017-12-12 16:07:18 ] INFO Plotting in png format...
[ 2017-12-12 16:07:18 ] INFO Plotting mds...
[ 2017-12-12 16:07:18 ] INFO Plotting biodetection...
[ 2017-12-12 16:07:19 ] INFO Plotting countsbio...
[ 2017-12-12 16:07:20 ] INFO Plotting saturation...
[ 2017-12-12 16:07:24 ] INFO Plotting readnoise...
[ 2017-12-12 16:07:25 ] INFO Plotting correl...
[ 2017-12-12 16:07:25 ] INFO Plotting pairwise...
[ 2017-12-12 16:07:34 ] INFO Plotting boxplot...
[ 2017-12-12 16:07:34 ] INFO Plotting gcbias...
[ 2017-12-12 16:07:34 ] INFO Plotting lengthbias...
[ 2017-12-12 16:07:35 ] INFO Plotting boxplot...
[ 2017-12-12 16:07:35 ] INFO Plotting gcbias...
[ 2017-12-12 16:07:36 ] INFO Plotting lengthbias...
[ 2017-12-12 16:07:36 ] INFO Plotting biodist...
[ 2017-12-12 16:07:36 ] INFO Contrast: Lbil22_vs_Lcil22
[ 2017-12-12 16:07:36 ] INFO Plotting volcano...
[ 2017-12-12 16:07:36 ] INFO Contrast: Lbil22_vs_Lcil22
[ 2017-12-12 16:07:37 ] INFO Plotting deheatmap...
[ 2017-12-12 16:07:37 ] INFO Contrast: Lbil22_vs_Lcil22
[ 2017-12-12 16:07:58 ] INFO Plotting filtered...
[ 2017-12-12 16:07:58 ] INFO Plotting in pdf format...
[ 2017-12-12 16:07:58 ] INFO Plotting mds...
[ 2017-12-12 16:07:58 ] INFO Plotting biodetection...
[ 2017-12-12 16:07:59 ] INFO Plotting countsbio...
[ 2017-12-12 16:08:00 ] INFO Plotting saturation...
[ 2017-12-12 16:08:03 ] INFO Plotting readnoise...
[ 2017-12-12 16:08:04 ] INFO Plotting correl...
[ 2017-12-12 16:08:04 ] INFO Plotting pairwise...
[ 2017-12-12 16:08:09 ] INFO Plotting boxplot...
[ 2017-12-12 16:08:09 ] INFO Plotting gcbias...
[ 2017-12-12 16:08:10 ] INFO Plotting lengthbias...
[ 2017-12-12 16:08:11 ] INFO Plotting boxplot...
[ 2017-12-12 16:08:11 ] INFO Plotting gcbias...
[ 2017-12-12 16:08:11 ] INFO Plotting lengthbias...
[ 2017-12-12 16:08:12 ] INFO Plotting biodist...
[ 2017-12-12 16:08:12 ] INFO Contrast: Lbil22_vs_Lcil22
[ 2017-12-12 16:08:12 ] INFO Plotting volcano...
[ 2017-12-12 16:08:12 ] INFO Contrast: Lbil22_vs_Lcil22
[ 2017-12-12 16:08:12 ] INFO Plotting deheatmap...
[ 2017-12-12 16:08:12 ] INFO Contrast: Lbil22_vs_Lcil22
[ 2017-12-12 16:08:35 ] INFO Plotting filtered...
[ 2017-12-12 16:08:35 ] INFO Creating HTML report...
[ 2017-12-12 16:08:35 ] INFO Compressing figures...

Figures

Multidimensional scaling

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

Biotype detection

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

Biotype detection counts

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

Read and biotype saturation

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

Read saturation per biotype for all samples

Read saturation per sample for all biotypes

RNA-Seq reads noise

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

Correlation plots

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

Correlation heatmap

Data correlogram

Pairwise scatterplots

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

Boxplots

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

Boxplot of un-normalized data

Boxplot of normalized data

GC content bias

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

GC content bias un-normalized

GC content bias normalized

Gene/transcript length bias

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

Gene/transcript length bias un-normalized

Gene/transcript length bias normalized

Chromosome and biotype distribution of filtered genes

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

Volcano plots

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

Volcano plot for the contrast Lbil22_vs_Lcil22

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 Lbil22_vs_Lcil22

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 Lbil22_vs_Lcil22

Get all the figures in png, pdf format.

Results

DEG table for the contrast Lbil22_vs_Lcil22

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 Lbil22_vs_Lcil22. 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_Lbil22_vs_Lcil22	log2_normalized_mean_counts_Lbil22	rpgm_normalized_mean_counts_Lbil22	log2_normalized_mean_counts_Lcil22	rpgm_normalized_mean_counts_Lcil22	log2_normalized_counts_SK3R16_Lbil22_7	log2_normalized_counts_SK3R17_Lbil22_8	log2_normalized_counts_SK3R18_Lbil22_9	rpgm_normalized_counts_SK3R16_Lbil22_7	rpgm_normalized_counts_SK3R17_Lbil22_8	rpgm_normalized_counts_SK3R18_Lbil22_9	log2_normalized_counts_SK3R22_Lcil22_8	log2_normalized_counts_SK3R23_Lcil22_11	log2_normalized_counts_SK3R24_Lcil22_12	rpgm_normalized_counts_SK3R22_Lcil22_8	rpgm_normalized_counts_SK3R23_Lcil22_11	rpgm_normalized_counts_SK3R24_Lcil22_12
chrX	73254540	73265390	ENSMUSG00000058147	0.4522	-	Xlr3c	protein_coding	3.88e-31	2.33e-27	-6.17991	6.59e+00	4.59e-02	3.33e-01	1.60e-04	6.49e+00	6.51e+00	6.77e+00	4.27e-02	4.31e-02	5.18e-02	0.00e+00	1.00e+00	0.00e+00	0.00e+00	4.79e-04	0.00e+00
chr17	32659410	32676687	ENSMUSG00000090700	0.4637	+	Cyp4f40	protein_coding	4.09e-31	2.33e-27	6.51907	1.06e+00	9.13e-04	7.74e+00	1.46e-01	1.58e+00	1.58e+00	0.00e+00	1.37e-03	1.37e-03	0.00e+00	7.87e+00	7.71e+00	7.63e+00	1.59e-01	1.43e-01	1.35e-01
chr15	84324026	84342978	ENSMUSG00000022439	0.5140	+	Parvg	protein_coding	1.82e-29	6.93e-26	6.43820	1.06e+00	2.00e-04	7.65e+00	3.02e-02	1.58e+00	1.58e+00	0.00e+00	3.00e-04	3.00e-04	0.00e+00	7.89e+00	7.58e+00	7.48e+00	3.54e-02	2.85e-02	2.67e-02
chr3	92385685	92387324	ENSMUSG00000046259	0.4207	+	Sprr2h	protein_coding	6.59e-25	1.88e-21	6.45615	5.28e-01	4.12e-04	7.19e+00	8.97e-02	0.00e+00	1.58e+00	0.00e+00	0.00e+00	1.23e-03	0.00e+00	7.26e+00	7.07e+00	7.25e+00	9.38e-02	8.21e-02	9.32e-02
chr10	70922832	71159700	ENSMUSG00000014329	0.4411	-	Bicc1	protein_coding	1.75e-22	4.00e-19	-5.07146	6.45e+00	1.46e-02	1.11e+00	2.74e-04	6.15e+00	6.27e+00	6.92e+00	1.15e-02	1.25e-02	1.97e-02	2.32e+00	0.00e+00	1.00e+00	6.58e-04	0.00e+00	1.65e-04
chr13	33879816	33905708	ENSMUSG00000052180	0.4287	-	Serpinb6c	protein_coding	1.98e-21	3.76e-18	5.19162	1.53e+00	1.95e-03	7.06e+00	9.72e-02	0.00e+00	2.00e+00	2.58e+00	0.00e+00	2.19e-03	3.65e-03	7.10e+00	7.19e+00	6.89e+00	9.93e-02	1.06e-01	8.62e-02
chr11	6506548	6520965	ENSMUSG00000020437	0.5216	-	Myo1g	protein_coding	8.66e-20	1.41e-16	4.93618	1.72e+00	1.32e-04	7.00e+00	5.23e-03	2.58e+00	2.58e+00	0.00e+00	1.99e-04	1.99e-04	0.00e+00	7.35e+00	7.19e+00	6.48e+00	6.44e-03	5.76e-03	3.50e-03
chr2	84998583	85037462	ENSMUSG00000027071	0.4700	-	P2rx3	protein_coding	1.21e-17	1.73e-14	4.60126	1.33e+00	6.36e-04	6.47e+00	2.10e-02	3.00e+00	1.00e+00	0.00e+00	1.67e-03	2.39e-04	0.00e+00	6.34e+00	6.58e+00	6.49e+00	1.91e-02	2.27e-02	2.12e-02
chr11	100203162	100207548	ENSMUSG00000045545	0.5250	-	Krt14	protein_coding	1.45e-17	1.84e-14	3.37304	4.26e+00	1.38e-02	7.71e+00	1.49e-01	4.81e+00	3.58e+00	4.39e+00	1.92e-02	7.83e-03	1.42e-02	7.50e+00	7.73e+00	7.89e+00	1.28e-01	1.51e-01	1.69e-01
chr7	30763756	30781063	ENSMUSG00000060962	0.5123	+	Dmkn	protein_coding	1.91e-16	2.18e-13	4.94753	1.06e+00	8.45e-04	5.28e+00	4.50e-02	1.58e+00	0.00e+00	1.58e+00	1.27e-03	0.00e+00	1.27e-03	4.25e+00	4.09e+00	7.49e+00	1.14e-02	1.01e-02	1.13e-01
chr2	122594467	122611303	ENSMUSG00000027199	0.4166	-	Gatm	protein_coding	2.92e-16	3.03e-13	5.91488	0.00e+00	0.00e+00	5.89e+00	2.48e-02	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	5.55e+00	6.00e+00	6.13e+00	1.92e-02	2.63e-02	2.88e-02
chr5	53107083	53213927	ENSMUSG00000029189	0.4585	-	Sel1l3	protein_coding	6.97e-16	6.62e-13	5.58496	3.33e-01	4.62e-05	5.99e+00	8.72e-03	0.00e+00	1.00e+00	0.00e+00	0.00e+00	1.38e-04	0.00e+00	6.17e+00	5.88e+00	5.93e+00	9.83e-03	8.03e-03	8.31e-03
chr7	19671579	19677941	ENSMUSG00000002992	0.5383	-	Apoc2	protein_coding	1.14e-15	9.29e-13	5.13516	1.06e+00	9.29e-04	6.33e+00	5.64e-02	1.58e+00	1.58e+00	0.00e+00	1.39e-03	1.39e-03	0.00e+00	5.98e+00	6.69e+00	6.32e+00	4.32e-02	7.10e-02	5.50e-02
chr7	19671584	19681423	ENSMUSG00000109350	0.5260	-	Gm44805	protein_coding	1.14e-15	9.29e-13	5.13516	1.06e+00	9.32e-04	6.33e+00	5.66e-02	1.58e+00	1.58e+00	0.00e+00	1.40e-03	1.40e-03	0.00e+00	5.98e+00	6.69e+00	6.32e+00	4.33e-02	7.13e-02	5.52e-02
chr10	127000709	127020994	ENSMUSG00000025432	0.5093	+	Avil	protein_coding	2.12e-15	1.61e-12	3.61471	3.06e+00	2.77e-03	6.84e+00	3.76e-02	2.58e+00	2.58e+00	4.00e+00	1.66e-03	1.66e-03	4.98e-03	6.86e+00	6.85e+00	6.81e+00	3.82e-02	3.79e-02	3.69e-02
chr8	104961718	104969537	ENSMUSG00000031877	0.4558	+	Ces2g	protein_coding	4.41e-15	3.14e-12	4.96000	1.19e+00	7.06e-04	6.36e+00	3.47e-02	0.00e+00	2.00e+00	1.58e+00	0.00e+00	1.27e-03	8.47e-04	6.66e+00	6.25e+00	6.17e+00	4.24e-02	3.18e-02	3.01e-02
chr4	138877942	138882817	ENSMUSG00000028751	0.5541	+	Pla2g2e	protein_coding	5.09e-15	3.41e-12	3.69076	2.82e+00	4.21e-03	6.63e+00	6.19e-02	3.46e+00	2.00e+00	3.00e+00	6.32e-03	1.90e-03	4.42e-03	6.55e+00	6.73e+00	6.60e+00	5.88e-02	6.64e-02	6.07e-02
chr3	106482405	106503030	ENSMUSG00000027901	0.4567	+	Dennd2d	protein_coding	8.74e-15	5.54e-12	3.06316	4.74e+00	2.72e-03	7.78e+00	2.35e-02	4.91e+00	4.39e+00	4.91e+00	3.03e-03	2.09e-03	3.03e-03	7.49e+00	7.58e+00	8.26e+00	1.87e-02	1.99e-02	3.19e-02
chr7	24349196	24355109	ENSMUSG00000030484	0.5176	+	Lypd5	protein_coding	1.83e-14	1.10e-11	5.08746	5.28e-01	2.49e-04	5.82e+00	2.08e-02	1.58e+00	0.00e+00	0.00e+00	7.48e-04	0.00e+00	0.00e+00	5.93e+00	5.70e+00	5.83e+00	2.24e-02	1.91e-02	2.10e-02
chr2	28926949	29055066	ENSMUSG00000079502	0.4912	-	Cfap77	protein_coding	1.08e-09	6.13e-07	4.25873	1.06e+00	1.27e-04	5.47e+00	4.15e-03	0.00e+00	1.58e+00	1.58e+00	0.00e+00	1.90e-04	1.90e-04	5.25e+00	5.67e+00	5.49e+00	3.52e-03	4.75e-03	4.18e-03
chr5	129096750	129204599	ENSMUSG00000044017	0.4945	+	Adgrd1	protein_coding	1.17e-09	6.29e-07	3.88536	1.19e+00	4.40e-04	5.40e+00	9.53e-03	2.58e+00	1.00e+00	0.00e+00	1.10e-03	2.20e-04	0.00e+00	4.81e+00	5.46e+00	5.93e+00	5.94e-03	9.45e-03	1.32e-02
chr11	100140810	100148665	ENSMUSG00000020911	0.5148	-	Krt19	protein_coding	1.21e-09	6.29e-07	2.55738	5.28e+00	1.65e-02	8.00e+00	9.88e-02	4.17e+00	5.81e+00	5.86e+00	6.51e-03	2.11e-02	2.18e-02	7.75e+00	8.26e+00	7.99e+00	8.23e-02	1.17e-01	9.69e-02
chr5	23850597	23855038	ENSMUSG00000097180	0.4644	+	2700038G22Rik	lincRNA	1.44e-09	7.15e-07	2.84645	2.97e+00	3.06e-03	5.20e+00	2.45e-02	3.46e+00	2.00e+00	3.46e+00	3.99e-03	1.20e-03	3.99e-03	5.88e+00	2.81e+00	6.92e+00	2.31e-02	2.39e-03	4.78e-02
chr11	99065678	99089306	ENSMUSG00000017607	0.5139	-	Tns4	protein_coding	2.67e-09	1.27e-06	4.64386	3.33e-01	5.32e-05	4.98e+00	5.16e-03	0.00e+00	1.00e+00	0.00e+00	0.00e+00	1.60e-04	0.00e+00	5.29e+00	4.25e+00	5.39e+00	6.07e-03	2.87e-03	6.54e-03
chr14	57275034	57398483	ENSMUSG00000021947	0.4565	-	Cryl1	protein_coding	6.08e-09	2.77e-06	4.62936	3.33e-01	2.00e-04	5.02e+00	1.92e-02	0.00e+00	1.00e+00	0.00e+00	0.00e+00	5.99e-04	0.00e+00	5.04e+00	5.32e+00	4.70e+00	1.92e-02	2.34e-02	1.50e-02
chr10	120361275	120476469	ENSMUSG00000056758	0.4419	-	Hmga2	protein_coding	6.90e-09	3.02e-06	2.20518	6.56e+00	1.69e-02	8.76e+00	7.86e-02	6.60e+00	6.66e+00	6.41e+00	1.74e-02	1.81e-02	1.52e-02	8.63e+00	8.69e+00	8.96e+00	7.14e-02	7.44e-02	9.00e-02
chr15	66891320	66923201	ENSMUSG00000005124	0.4687	+	Wisp1	protein_coding	8.93e-09	3.77e-06	-2.60204	6.39e+00	1.08e-02	3.67e+00	1.68e-03	6.49e+00	6.04e+00	6.63e+00	1.15e-02	8.38e-03	1.26e-02	3.81e+00	4.39e+00	2.81e+00	1.68e-03	2.58e-03	7.74e-04
chr4	108879063	108943324	ENSMUSG00000003411	0.4534	+	Rab3b	protein_coding	1.14e-08	4.63e-06	4.77259	0.00e+00	0.00e+00	4.72e+00	6.99e-03	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	5.25e+00	4.46e+00	4.46e+00	9.83e-03	5.58e-03	5.58e-03
chrX	160623094	160735765	ENSMUSG00000062168	0.3911	-	Ppef1	protein_coding	1.19e-08	4.67e-06	2.89616	3.10e+00	1.92e-03	6.06e+00	1.58e-02	2.58e+00	3.00e+00	3.70e+00	1.20e-03	1.68e-03	2.88e-03	6.11e+00	5.93e+00	6.15e+00	1.63e-02	1.44e-02	1.68e-02
chr3	138217760	138233382	ENSMUSG00000055301	0.4321	+	Adh7	protein_coding	1.65e-08	6.26e-06	-1.62986	1.14e+01	6.08e-01	9.85e+00	1.96e-01	1.12e+01	1.20e+01	1.11e+01	5.01e-01	8.66e-01	4.58e-01	9.95e+00	9.80e+00	9.79e+00	2.11e-01	1.90e-01	1.88e-01
chr11	72961169	72993044	ENSMUSG00000020788	0.5179	+	Atp2a3	protein_coding	1.71e-08	6.27e-06	2.68353	2.97e+00	1.96e-03	6.06e+00	1.37e-02	4.52e+00	2.81e+00	1.58e+00	4.31e-03	1.18e-03	3.92e-04	5.36e+00	6.27e+00	6.55e+00	7.84e-03	1.49e-02	1.82e-02
chr8	62057042	62123150	ENSMUSG00000031635	0.3669	-	Anxa10	protein_coding	1.84e-08	6.57e-06	4.14296	9.36e-01	6.23e-04	5.73e+00	1.62e-02	0.00e+00	2.81e+00	0.00e+00	0.00e+00	1.87e-03	0.00e+00	5.81e+00	5.73e+00	5.64e+00	1.71e-02	1.62e-02	1.53e-02
chr10	24595442	24598683	ENSMUSG00000019997	0.5142	+	Ctgf	protein_coding	2.59e-08	8.93e-06	-2.33608	7.83e+00	5.78e-02	5.55e+00	1.13e-02	8.37e+00	7.22e+00	7.89e+00	8.02e-02	3.60e-02	5.73e-02	5.78e+00	5.32e+00	5.55e+00	1.31e-02	9.48e-03	1.12e-02
chr15	37366175	37792570	ENSMUSG00000051359	0.4433	-	Ncald	protein_coding	2.66e-08	8.93e-06	-3.33787	4.91e+00	2.55e-03	1.53e+00	1.74e-04	4.81e+00	5.13e+00	4.81e+00	2.35e-03	2.96e-03	2.35e-03	2.00e+00	1.58e+00	1.00e+00	2.61e-04	1.74e-04	8.70e-05
chr11	16971206	17052381	ENSMUSG00000020120	0.4175	-	Plek	protein_coding	3.12e-08	1.01e-05	4.44294	3.33e-01	6.11e-05	4.78e+00	5.14e-03	0.00e+00	1.00e+00	0.00e+00	0.00e+00	1.83e-04	0.00e+00	5.43e+00	4.32e+00	4.58e+00	7.70e-03	3.48e-03	4.22e-03
chr2	6539694	7509563	ENSMUSG00000002107	0.4013	-	Celf2	protein_coding	4.27e-08	1.35e-05	-2.96963	5.55e+00	1.75e-03	2.46e+00	1.90e-04	5.75e+00	5.46e+00	5.43e+00	2.02e-03	1.64e-03	1.60e-03	3.00e+00	1.58e+00	2.81e+00	2.66e-04	7.61e-05	2.28e-04
chr13	4268172	4279433	ENSMUSG00000021211	0.3718	-	Akr1c12	protein_coding	4.71e-08	1.45e-05	2.00951	5.97e+00	4.22e-02	7.98e+00	1.72e-01	6.04e+00	5.86e+00	6.02e+00	4.42e-02	3.88e-02	4.35e-02	7.79e+00	8.04e+00	8.11e+00	1.50e-01	1.78e-01	1.88e-01
chr2	60598292	60722643	ENSMUSG00000026971	0.4310	-	Itgb6	protein_coding	1.08e-07	3.25e-05	-2.42502	6.56e+00	8.06e-03	4.17e+00	1.43e-03	6.57e+00	6.15e+00	6.95e+00	7.92e-03	5.90e-03	1.04e-02	4.17e+00	4.25e+00	4.09e+00	1.43e-03	1.52e-03	1.35e-03
chr11	32283511	32284465	ENSMUSG00000069919	0.5717	+	Hba-a1	protein_coding	1.17e-07	3.42e-05	4.01495	8.62e-01	7.53e-04	5.01e+00	2.36e-02	1.58e+00	1.00e+00	0.00e+00	1.51e-03	7.53e-04	0.00e+00	4.95e+00	4.86e+00	5.21e+00	2.26e-02	2.11e-02	2.71e-02
chr9	7272514	7283331	ENSMUSG00000050578	0.3772	+	Mmp13	protein_coding	1.54e-07	4.38e-05	4.35755	3.33e-01	1.35e-04	4.77e+00	1.07e-02	0.00e+00	1.00e+00	0.00e+00	0.00e+00	4.05e-04	0.00e+00	4.95e+00	4.64e+00	4.70e+00	1.22e-02	9.73e-03	1.01e-02
chr19	47083471	47090625	ENSMUSG00000071528	0.4299	-	Usmg5	protein_coding	1.64e-07	4.56e-05	-1.11353	9.95e+00	4.88e-01	8.92e+00	2.25e-01	9.69e+00	1.10e+01	9.16e+00	3.51e-01	8.69e-01	2.43e-01	8.01e+00	9.54e+00	9.21e+00	1.09e-01	3.16e-01	2.51e-01
chr1	90915085	90951142	ENSMUSG00000026303	0.4978	+	Mlph	protein_coding	1.70e-07	4.60e-05	4.62449	0.00e+00	0.00e+00	4.58e+00	3.76e-03	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	4.25e+00	5.04e+00	4.46e+00	2.86e-03	5.08e-03	3.34e-03
chrX	140907608	140939472	ENSMUSG00000031430	0.3902	+	Vsig1	protein_coding	1.85e-07	4.90e-05	4.26303	5.28e-01	2.50e-04	5.00e+00	1.16e-02	0.00e+00	1.58e+00	0.00e+00	0.00e+00	7.49e-04	0.00e+00	4.91e+00	5.04e+00	5.04e+00	1.09e-02	1.20e-02	1.20e-02
chr10	128958277	128960988	ENSMUSG00000025347	0.4967	-	Mettl7b	protein_coding	1.99e-07	5.15e-05	3.87036	1.19e+00	9.83e-04	5.21e+00	2.24e-02	0.00e+00	2.00e+00	1.58e+00	0.00e+00	1.77e-03	1.18e-03	5.49e+00	4.52e+00	5.61e+00	2.59e-02	1.30e-02	2.83e-02
chr7	110627661	110629820	ENSMUSG00000030790	0.5301	+	Adm	protein_coding	2.10e-07	5.32e-05	-2.30558	6.83e+00	3.67e-02	4.49e+00	7.17e-03	7.03e+00	6.34e+00	7.12e+00	4.11e-02	2.53e-02	4.37e-02	4.17e+00	4.17e+00	5.13e+00	5.38e-03	5.38e-03	1.08e-02
chr14	37102140	37114022	ENSMUSG00000096001	0.4574	-	2610528A11Rik	protein_coding	2.44e-07	6.05e-05	-1.87447	6.87e+00	6.76e-02	4.97e+00	1.80e-02	6.64e+00	6.94e+00	7.01e+00	5.76e-02	7.09e-02	7.44e-02	4.52e+00	5.21e+00	5.17e+00	1.28e-02	2.09e-02	2.03e-02
chr5	143270075	143272513	ENSMUSG00000105345	0.5092	+	BC030343	lincRNA	2.61e-07	6.33e-05	-2.49825	5.16e+00	2.92e-02	2.50e+00	4.52e-03	4.64e+00	5.78e+00	5.04e+00	1.91e-02	4.31e-02	2.55e-02	3.58e+00	1.58e+00	2.32e+00	8.77e-03	1.59e-03	3.19e-03
chr5	89417522	89457898	ENSMUSG00000035540	0.4032	-	Gc	protein_coding	2.85e-07	6.76e-05	-1.94847	6.09e+00	1.93e-02	4.17e+00	4.81e-03	6.49e+00	6.52e+00	5.25e+00	2.38e-02	2.43e-02	9.89e-03	3.81e+00	3.91e+00	4.81e+00	3.48e-03	3.74e-03	7.22e-03
chr15	55112317	55259271	ENSMUSG00000022419	0.4432	+	Deptor	protein_coding	3.41e-07	7.94e-05	1.96171	5.11e+00	3.49e-03	7.09e+00	1.39e-02	5.39e+00	4.91e+00	5.04e+00	4.21e-03	2.98e-03	3.29e-03	7.20e+00	7.04e+00	7.02e+00	1.50e-02	1.34e-02	1.32e-02
chr5	134600022	134615025	ENSMUSG00000040751	0.5223	-	Lat2	protein_coding	4.22e-07	9.61e-05	2.36500	3.35e+00	1.43e-03	5.77e+00	7.97e-03	3.46e+00	2.58e+00	4.00e+00	1.43e-03	7.16e-04	2.15e-03	5.21e+00	6.04e+00	6.07e+00	5.16e-03	9.31e-03	9.45e-03
chr16	57624258	57754737	ENSMUSG00000068196	0.3956	-	Col8a1	protein_coding	4.34e-07	9.71e-05	-1.69764	6.98e+00	3.27e-02	5.31e+00	9.90e-03	6.67e+00	7.30e+00	6.98e+00	2.59e-02	4.02e-02	3.20e-02	5.43e+00	5.25e+00	5.25e+00	1.07e-02	9.47e-03	9.47e-03
chr2	164354070	164389095	ENSMUSG00000017002	0.4508	-	Slpi	protein_coding	4.57e-07	1.00e-04	2.01878	6.23e+00	2.01e-02	8.27e+00	8.22e-02	5.88e+00	6.54e+00	6.27e+00	1.55e-02	2.45e-02	2.03e-02	8.34e+00	8.28e+00	8.19e+00	8.61e-02	8.26e-02	7.78e-02
chr13	4191184	4205596	ENSMUSG00000021213	0.3669	+	Akr1c13	protein_coding	4.79e-07	1.03e-04	1.73541	6.93e+00	1.26e-02	8.67e+00	4.21e-02	6.93e+00	6.70e+00	7.17e+00	1.24e-02	1.06e-02	1.47e-02	8.42e+00	8.83e+00	8.76e+00	3.52e-02	4.66e-02	4.46e-02
chr7	72648757	72649311	ENSMUSG00000081895	0.4757	-	Rpl17-ps10	processed_pseudogene	5.27e-07	1.11e-04	-0.84632	7.88e+00	2.74e-01	7.53e+00	1.52e-01	7.72e+00	9.52e+00	6.41e+00	1.67e-01	5.86e-01	6.70e-02	7.04e+00	7.99e+00	7.55e+00	1.04e-01	2.03e-01	1.48e-01
chr2	14229392	14332057	ENSMUSG00000026712	0.4010	+	Mrc1	protein_coding	5.61e-07	1.15e-04	1.79538	6.91e+00	5.62e-02	8.71e+00	1.96e-01	7.07e+00	6.85e+00	6.83e+00	6.23e-02	5.34e-02	5.29e-02	8.56e+00	8.63e+00	8.92e+00	1.76e-01	1.85e-01	2.27e-01
chr9	102626296	102634425	ENSMUSG00000035048	0.4761	+	Anapc13	protein_coding	5.68e-07	1.15e-04	-1.39384	7.24e+00	5.61e-02	5.88e+00	2.11e-02	6.93e+00	7.83e+00	6.95e+00	4.33e-02	8.09e-02	4.40e-02	5.46e+00	6.15e+00	6.02e+00	1.54e-02	2.51e-02	2.29e-02
chr17	35059035	35062095	ENSMUSG00000007039	0.5779	+	Ddah2	protein_coding	5.81e-07	1.15e-04	-1.83076	7.27e+00	5.18e-02	5.22e+00	1.43e-02	7.51e+00	7.23e+00	7.07e+00	6.08e-02	5.00e-02	4.47e-02	4.70e+00	4.64e+00	6.32e+00	8.39e-03	8.06e-03	2.65e-02
chr5	90786103	90789600	ENSMUSG00000029379	0.4294	+	Cxcl3	protein_coding	5.83e-07	1.15e-04	-1.77259	5.55e+00	1.70e-02	3.94e+00	4.76e-03	5.61e+00	6.52e+00	4.52e+00	1.52e-02	2.89e-02	6.98e-03	4.46e+00	3.91e+00	3.46e+00	6.66e-03	4.44e-03	3.17e-03
chr3	106759921	106790149	ENSMUSG00000040747	0.3689	-	Cd53	protein_coding	6.63e-07	1.28e-04	2.89162	2.09e+00	2.50e-03	5.53e+00	2.15e-02	3.46e+00	2.81e+00	0.00e+00	4.68e-03	2.81e-03	0.00e+00	5.86e+00	5.21e+00	5.52e+00	2.67e-02	1.68e-02	2.11e-02
chr8	70152778	70167488	ENSMUSG00000079033	0.5308	+	Mef2b	protein_coding	7.53e-07	1.43e-04	3.25634	1.19e+00	7.43e-04	4.83e+00	1.03e-02	0.00e+00	1.00e+00	2.58e+00	0.00e+00	3.71e-04	1.86e-03	5.00e+00	4.91e+00	4.58e+00	1.15e-02	1.08e-02	8.54e-03
chr13	12790822	12800078	ENSMUSG00000056457	0.3565	-	Prl2c3	protein_coding	9.64e-07	1.80e-04	1.75349	6.99e+00	9.27e-02	8.74e+00	3.14e-01	7.03e+00	6.91e+00	7.03e+00	9.54e-02	8.73e-02	9.54e-02	8.69e+00	8.92e+00	8.61e+00	3.02e-01	3.54e-01	2.87e-01
chr10	83600033	83648738	ENSMUSG00000020263	0.4828	-	Appl2	protein_coding	9.79e-07	1.80e-04	1.96347	4.28e+00	1.11e-03	6.22e+00	4.50e-03	4.64e+00	3.81e+00	4.39e+00	1.40e-03	7.61e-04	1.17e-03	6.75e+00	5.67e+00	6.23e+00	6.26e-03	2.93e-03	4.33e-03
chr8	70016123	70042734	ENSMUSG00000031858	0.4841	-	Mau2	protein_coding	1.02e-06	1.84e-04	2.43296	3.11e+00	7.07e-04	5.22e+00	4.16e-03	4.17e+00	2.58e+00	2.58e+00	1.33e-03	3.93e-04	3.93e-04	3.17e+00	6.09e+00	6.41e+00	6.28e-04	5.26e-03	6.59e-03
chr15	34484021	34495255	ENSMUSG00000022323	0.4078	-	Rida	protein_coding	1.10e-06	1.97e-04	-1.39261	1.06e+01	3.86e-01	9.27e+00	1.47e-01	1.02e+01	1.11e+01	1.06e+01	2.86e-01	5.05e-01	3.68e-01	9.04e+00	9.31e+00	9.46e+00	1.25e-01	1.50e-01	1.66e-01
chr17	39846958	39848788	ENSMUSG00000098178	0.6019	+	Gm42418	lincRNA	1.22e-06	2.14e-04	1.93407	6.58e+00	7.66e-02	8.49e+00	2.95e-01	6.46e+00	6.88e+00	6.41e+00	6.94e-02	9.33e-02	6.70e-02	8.25e+00	8.26e+00	8.97e+00	2.42e-01	2.44e-01	3.99e-01
chr9	71110659	71168682	ENSMUSG00000032204	0.4535	-	Aqp9	protein_coding	1.24e-06	2.15e-04	3.05528	2.33e+00	4.77e-04	5.46e+00	4.77e-03	2.58e+00	2.81e+00	1.58e+00	5.51e-04	6.61e-04	2.20e-04	5.70e+00	5.21e+00	5.46e+00	5.62e-03	3.96e-03	4.73e-03
chr7	45554893	45567752	ENSMUSG00000030825	0.5088	+	Hsd17b14	protein_coding	1.35e-06	2.30e-04	2.01668	3.70e+00	1.64e-03	5.86e+00	7.02e-03	3.91e+00	2.81e+00	4.39e+00	1.72e-03	7.39e-04	2.46e-03	5.91e+00	5.88e+00	5.78e+00	7.27e-03	7.14e-03	6.65e-03
chr5	100553725	100572245	ENSMUSG00000029322	0.4460	-	Plac8	protein_coding	1.37e-06	2.30e-04	1.45014	6.40e+00	2.34e-02	7.90e+00	6.45e-02	6.67e+00	5.67e+00	6.86e+00	2.67e-02	1.32e-02	3.04e-02	7.44e+00	8.14e+00	8.12e+00	4.57e-02	7.43e-02	7.35e-02
chr1	83116766	83119167	ENSMUSG00000026166	0.3926	+	Ccl20	protein_coding	1.44e-06	2.39e-04	-1.91368	6.94e+00	5.35e-02	5.02e+00	1.39e-02	6.87e+00	6.75e+00	7.21e+00	5.03e-02	4.64e-02	6.38e-02	4.70e+00	5.04e+00	5.32e+00	1.08e-02	1.39e-02	1.69e-02
chr6	34354119	34368463	ENSMUSG00000029762	0.4523	+	Akr1b8	protein_coding	1.58e-06	2.57e-04	1.84865	6.30e+00	2.89e-02	8.21e+00	1.05e-01	5.70e+00	6.52e+00	6.67e+00	1.82e-02	3.25e-02	3.60e-02	8.22e+00	8.15e+00	8.24e+00	1.06e-01	1.01e-01	1.08e-01
chr18	70240429	70244584	ENSMUSG00000024512	0.3660	-	Dynap	protein_coding	1.97e-06	3.15e-04	2.09954	3.73e+00	7.25e-03	5.90e+00	3.29e-02	4.32e+00	3.17e+00	3.70e+00	1.06e-02	4.46e-03	6.69e-03	5.70e+00	5.88e+00	6.11e+00	2.84e-02	3.23e-02	3.79e-02
chr7	45729983	45784669	ENSMUSG00000003271	0.4940	-	Sult2b1	protein_coding	1.99e-06	3.15e-04	2.24176	3.50e+00	1.79e-03	5.85e+00	9.07e-03	4.32e+00	3.17e+00	3.00e+00	3.01e-03	1.27e-03	1.11e-03	5.52e+00	5.98e+00	6.04e+00	7.12e-03	9.81e-03	1.03e-02
chr13	12996132	13005383	ENSMUSG00000079092	0.3558	-	Prl2c2	protein_coding	2.01e-06	3.15e-04	1.71862	7.51e+00	1.33e-01	9.22e+00	4.41e-01	7.47e+00	7.48e+00	7.57e+00	1.30e-01	1.31e-01	1.39e-01	9.15e+00	9.42e+00	9.09e+00	4.19e-01	5.03e-01	4.01e-01
chr7	140967221	140969825	ENSMUSG00000025491	0.4868	+	Ifitm1	protein_coding	2.58e-06	3.98e-04	2.63346	3.50e+00	8.26e-03	6.33e+00	5.48e-02	3.46e+00	4.46e+00	2.58e+00	6.88e-03	1.45e-02	3.44e-03	6.48e+00	6.25e+00	6.27e+00	6.06e-02	5.16e-02	5.23e-02
chr2	32384633	32388252	ENSMUSG00000026822	0.5470	-	Lcn2	protein_coding	2.83e-06	4.30e-04	2.13430	3.70e+00	2.41e-03	5.88e+00	1.12e-02	4.32e+00	3.32e+00	3.46e+00	3.62e-03	1.71e-03	1.91e-03	6.13e+00	6.02e+00	5.49e+00	1.31e-02	1.22e-02	8.38e-03
chr2	170346991	170427845	ENSMUSG00000013523	0.4619	-	Bcas1	protein_coding	3.40e-06	5.10e-04	4.32193	0.00e+00	0.00e+00	4.23e+00	3.91e-03	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	4.58e+00	4.64e+00	3.46e+00	4.73e-03	4.94e-03	2.06e-03
chr2	49619298	49774778	ENSMUSG00000026764	0.4546	+	Kif5c	protein_coding	3.60e-06	5.32e-04	2.07039	4.04e+00	1.58e-03	6.11e+00	6.97e-03	4.32e+00	3.81e+00	4.00e+00	1.92e-03	1.31e-03	1.52e-03	6.00e+00	5.88e+00	6.44e+00	6.37e-03	5.86e-03	8.69e-03
chr2	118111876	118127133	ENSMUSG00000040152	0.4701	+	Thbs1	protein_coding	3.73e-06	5.46e-04	-2.72085	5.77e+00	1.26e-02	2.99e+00	1.73e-03	6.04e+00	4.91e+00	6.36e+00	1.41e-02	6.29e-03	1.76e-02	3.81e+00	3.17e+00	2.00e+00	2.82e-03	1.73e-03	6.50e-04
chr8	54077606	54187096	ENSMUSG00000031520	0.3722	+	Vegfc	protein_coding	4.32e-06	6.23e-04	-2.82443	4.81e+00	5.71e-03	1.97e+00	6.26e-04	4.64e+00	4.75e+00	5.04e+00	5.01e-03	5.43e-03	6.68e-03	2.32e+00	2.00e+00	1.58e+00	8.35e-04	6.26e-04	4.18e-04
chr6	58584523	58695676	ENSMUSG00000029802	0.3979	+	Abcg2	protein_coding	4.82e-06	6.87e-04	-2.56986	4.90e+00	2.27e-03	2.25e+00	3.21e-04	4.17e+00	5.29e+00	5.25e+00	1.26e-03	2.81e-03	2.74e-03	3.17e+00	1.58e+00	2.00e+00	5.93e-04	1.48e-04	2.22e-04
chr3	152273849	152340407	ENSMUSG00000054942	0.4577	-	Miga1	protein_coding	5.32e-06	7.48e-04	-2.00922	5.69e+00	3.04e-03	3.66e+00	7.10e-04	5.81e+00	5.93e+00	5.32e+00	3.25e-03	3.55e-03	2.31e-03	4.00e+00	3.81e+00	3.17e+00	8.87e-04	7.69e-04	4.73e-04
chr6	40401375	40436135	ENSMUSG00000037172	0.4571	-	E330009J07Rik	protein_coding	6.83e-06	9.49e-04	2.02628	4.49e+00	1.94e-03	6.52e+00	8.20e-03	4.17e+00	4.58e+00	4.70e+00	1.53e-03	2.06e-03	2.24e-03	6.63e+00	6.58e+00	6.36e+00	8.79e-03	8.52e-03	7.27e-03
chr4	134356372	134383850	ENSMUSG00000028838	0.5308	-	Extl1	protein_coding	7.19e-06	9.87e-04	2.93289	1.33e+00	5.29e-04	4.81e+00	5.36e-03	0.00e+00	1.00e+00	3.00e+00	0.00e+00	1.98e-04	1.39e-03	4.70e+00	4.86e+00	4.86e+00	4.96e-03	5.56e-03	5.56e-03
chr17	14665500	14694262	ENSMUSG00000023885	0.4651	-	Thbs2	protein_coding	7.43e-06	1.01e-03	-2.33703	4.96e+00	9.49e-03	2.66e+00	1.63e-03	4.52e+00	5.32e+00	5.04e+00	6.73e-03	1.19e-02	9.79e-03	2.58e+00	2.81e+00	2.58e+00	1.53e-03	1.84e-03	1.53e-03
chr7	25461707	25477603	ENSMUSG00000074272	0.4613	-	Ceacam1	protein_coding	7.69e-06	1.03e-03	4.22239	0.00e+00	0.00e+00	4.10e+00	1.65e-03	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	4.81e+00	3.32e+00	4.17e+00	2.52e-03	8.39e-04	1.59e-03
chr13	91796533	91807705	ENSMUSG00000021621	0.4352	-	Zcchc9	protein_coding	7.86e-06	1.04e-03	-0.77656	8.02e+00	9.00e-02	7.78e+00	5.25e-02	8.02e+00	9.72e+00	6.32e+00	5.91e-02	1.93e-01	1.81e-02	8.37e+00	7.53e+00	7.44e+00	7.56e-02	4.22e-02	3.96e-02
chrX	136072099	136085255	ENSMUSG00000057000	0.4125	-	Nxf3	protein_coding	8.08e-06	1.06e-03	-3.26303	3.98e+00	4.09e-03	6.67e-01	1.82e-04	3.70e+00	4.25e+00	4.00e+00	3.27e-03	4.91e-03	4.09e-03	1.00e+00	1.00e+00	0.00e+00	2.73e-04	2.73e-04	0.00e+00
chr6	124913681	124920103	ENSMUSG00000030122	0.5793	-	Ptms	protein_coding	9.46e-06	1.23e-03	1.47908	8.66e+00	1.35e-01	1.01e+01	3.76e-01	8.84e+00	8.42e+00	8.72e+00	1.52e-01	1.13e-01	1.40e-01	1.01e+01	1.02e+01	1.02e+01	3.53e-01	3.96e-01	3.80e-01
chr9	79598991	79718831	ENSMUSG00000032332	0.3950	-	Col12a1	protein_coding	1.06e-05	1.35e-03	-3.22239	4.14e+00	1.99e-03	8.62e-01	1.13e-04	3.46e+00	4.25e+00	4.70e+00	1.13e-03	2.03e-03	2.82e-03	0.00e+00	1.58e+00	1.00e+00	0.00e+00	2.26e-04	1.13e-04
chr1	120602418	120607992	ENSMUSG00000058665	0.5469	+	En1	protein_coding	1.11e-05	1.41e-03	-1.92600	5.98e+00	3.03e-02	3.88e+00	7.62e-03	6.00e+00	5.81e+00	6.13e+00	3.06e-02	2.67e-02	3.35e-02	3.32e+00	4.86e+00	3.46e+00	4.38e-03	1.36e-02	4.86e-03
chr17	39843013	39846341	ENSMUSG00000097971	0.6912	+	Gm26917	lincRNA	1.26e-05	1.58e-03	1.85244	6.43e+00	6.59e-02	8.20e+00	2.40e-01	6.00e+00	7.01e+00	6.27e+00	4.67e-02	9.48e-02	5.63e-02	8.34e+00	7.33e+00	8.94e+00	2.39e-01	1.19e-01	3.63e-01
chr18	38838673	38929404	ENSMUSG00000036585	0.4647	-	Fgf1	protein_coding	1.35e-05	1.67e-03	4.22239	0.00e+00	0.00e+00	4.21e+00	2.13e-03	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	4.17e+00	4.46e+00	4.00e+00	2.05e-03	2.53e-03	1.81e-03
chr12	73901375	73947530	ENSMUSG00000021109	0.3791	+	Hif1a	protein_coding	1.46e-05	1.79e-03	1.70627	6.82e+00	1.52e-02	8.52e+00	4.99e-02	6.60e+00	7.11e+00	6.74e+00	1.29e-02	1.84e-02	1.42e-02	8.80e+00	8.46e+00	8.32e+00	5.97e-02	4.72e-02	4.27e-02
chr10	76708792	76726168	ENSMUSG00000001119	0.5416	-	Col6a1	protein_coding	1.53e-05	1.85e-03	2.36923	3.70e+00	3.97e-03	6.14e+00	2.18e-02	3.00e+00	4.39e+00	3.70e+00	2.14e-03	6.11e-03	3.66e-03	6.38e+00	6.36e+00	5.70e+00	2.50e-02	2.47e-02	1.56e-02
chrX	101264378	101268255	ENSMUSG00000031304	0.4814	-	Il2rg	protein_coding	1.71e-05	2.06e-03	3.56071	5.28e-01	1.22e-04	4.27e+00	3.42e-03	0.00e+00	0.00e+00	1.58e+00	0.00e+00	0.00e+00	3.67e-04	4.64e+00	4.17e+00	4.00e+00	4.40e-03	3.12e-03	2.75e-03
chr7	30589681	30598909	ENSMUSG00000006310	0.4941	-	Zbtb32	protein_coding	1.76e-05	2.09e-03	2.26946	3.17e+00	3.02e-03	5.48e+00	1.60e-02	3.70e+00	2.81e+00	3.00e+00	4.35e-03	2.18e-03	2.54e-03	5.29e+00	5.55e+00	5.61e+00	1.38e-02	1.67e-02	1.74e-02
chr6	142490249	142507957	ENSMUSG00000030246	0.4421	-	Ldhb	protein_coding	1.79e-05	2.10e-03	-1.97575	5.24e+00	1.35e-02	3.25e+00	3.16e-03	4.64e+00	5.49e+00	5.58e+00	8.43e-03	1.55e-02	1.65e-02	2.58e+00	3.70e+00	3.46e+00	1.76e-03	4.21e-03	3.51e-03
chr10	82224850	82241280	ENSMUSG00000062931	0.3968	-	Zfp938	protein_coding	1.91e-05	2.22e-03	-1.30426	7.54e+00	4.62e-02	6.23e+00	1.86e-02	7.70e+00	7.79e+00	7.12e+00	5.07e-02	5.39e-02	3.38e-02	5.83e+00	6.44e+00	6.43e+00	1.37e-02	2.11e-02	2.08e-02
chr11	116336353	116413032	ENSMUSG00000052949	0.4806	-	Rnf157	protein_coding	2.00e-05	2.30e-03	-1.98751	6.24e+00	6.72e-03	4.26e+00	1.63e-03	6.49e+00	5.91e+00	6.32e+00	7.91e-03	5.24e-03	7.02e-03	4.39e+00	4.00e+00	4.39e+00	1.78e-03	1.33e-03	1.78e-03
chrX	143518591	143797796	ENSMUSG00000031284	0.3871	+	Pak3	protein_coding	2.02e-05	2.30e-03	-2.37346	5.24e+00	2.90e-03	2.66e+00	4.97e-04	5.46e+00	5.21e+00	5.04e+00	3.37e-03	2.83e-03	2.51e-03	3.58e+00	1.58e+00	2.81e+00	8.63e-04	1.57e-04	4.71e-04
chrX	20870166	20874735	ENSMUSG00000001131	0.4696	+	Timp1	protein_coding	2.12e-05	2.40e-03	1.91917	5.65e+00	3.76e-02	7.61e+00	1.44e-01	5.32e+00	6.19e+00	5.43e+00	2.87e-02	5.31e-02	3.10e-02	7.32e+00	7.76e+00	7.73e+00	1.17e-01	1.59e-01	1.56e-01
chr15	102028216	102032026	ENSMUSG00000023043	0.5377	+	Krt18	protein_coding	2.21e-05	2.48e-03	1.45259	7.84e+00	1.74e-01	9.26e+00	4.78e-01	7.81e+00	7.65e+00	8.06e+00	1.70e-01	1.52e-01	2.01e-01	8.74e+00	9.49e+00	9.55e+00	3.23e-01	5.43e-01	5.68e-01
chr5	142960343	142973185	ENSMUSG00000029581	0.5442	+	Fscn1	protein_coding	2.34e-05	2.59e-03	-2.15955	6.42e+00	2.36e-02	4.29e+00	5.09e-03	6.46e+00	5.73e+00	7.09e+00	2.25e-02	1.35e-02	3.50e-02	4.58e+00	3.58e+00	4.70e+00	5.96e-03	2.85e-03	6.47e-03
chr1	131638306	131675505	ENSMUSG00000004552	0.4674	+	Ctse	protein_coding	2.51e-05	2.75e-03	1.92600	3.69e+00	4.02e-03	5.65e+00	1.62e-02	3.91e+00	3.17e+00	4.00e+00	4.56e-03	2.61e-03	4.89e-03	5.52e+00	5.93e+00	5.49e+00	1.47e-02	1.96e-02	1.43e-02
chr8	45996902	46005707	ENSMUSG00000050914	0.4295	-	Ankrd37	protein_coding	2.59e-05	2.80e-03	-1.25493	7.55e+00	3.01e-02	6.29e+00	1.25e-02	7.35e+00	7.83e+00	7.48e+00	2.59e-02	3.61e-02	2.83e-02	5.98e+00	6.60e+00	6.29e+00	9.90e-03	1.53e-02	1.23e-02
chr2	168634438	168742409	ENSMUSG00000027546	0.5089	-	Atp9a	protein_coding	2.60e-05	2.80e-03	4.04439	3.33e-01	4.99e-05	4.43e+00	3.14e-03	0.00e+00	1.00e+00	0.00e+00	0.00e+00	1.50e-04	0.00e+00	4.00e+00	4.58e+00	4.70e+00	2.25e-03	3.44e-03	3.74e-03
chr6	29319140	29336019	ENSMUSG00000039742	0.4618	+	Fam71f1	protein_coding	2.63e-05	2.80e-03	-1.92015	6.32e+00	9.25e-03	4.39e+00	2.36e-03	6.29e+00	5.93e+00	6.74e+00	8.80e-03	6.85e-03	1.21e-02	3.91e+00	4.39e+00	4.86e+00	1.60e-03	2.28e-03	3.20e-03
chr2	39001580	39005624	ENSMUSG00000062997	0.5350	-	Rpl35	protein_coding	2.85e-05	3.01e-03	-0.84288	9.95e+00	4.49e-01	9.15e+00	2.50e-01	1.01e+01	1.09e+01	8.90e+00	4.21e-01	7.38e-01	1.87e-01	8.12e+00	9.80e+00	9.54e+00	1.09e-01	3.49e-01	2.92e-01
chr2	10094593	10131396	ENSMUSG00000037254	0.4259	-	Itih2	protein_coding	2.90e-05	3.03e-03	3.85798	3.33e-01	8.50e-05	4.19e+00	4.67e-03	0.00e+00	1.00e+00	0.00e+00	0.00e+00	2.55e-04	0.00e+00	4.86e+00	3.91e+00	3.81e+00	7.14e-03	3.57e-03	3.31e-03
chr7	25802475	25816913	ENSMUSG00000040703	0.5118	-	Cyp2s1	protein_coding	2.92e-05	3.03e-03	2.92892	1.72e+00	6.45e-04	4.95e+00	6.20e-03	2.58e+00	2.58e+00	0.00e+00	9.68e-04	9.68e-04	0.00e+00	4.58e+00	4.58e+00	5.67e+00	4.45e-03	4.45e-03	9.68e-03
chr8	123653929	123663884	ENSMUSG00000039960	0.5037	+	Rhou	protein_coding	3.17e-05	3.26e-03	-1.48699	5.87e+00	1.20e-02	4.39e+00	4.14e-03	5.32e+00	6.49e+00	5.78e+00	7.68e-03	1.75e-02	1.06e-02	4.75e+00	3.70e+00	4.70e+00	5.12e-03	2.36e-03	4.93e-03
chr3	55242364	55539068	ENSMUSG00000027797	0.4253	+	Dclk1	protein_coding	3.71e-05	3.75e-03	-1.80735	5.86e+00	2.79e-03	4.05e+00	7.61e-04	5.75e+00	5.88e+00	5.95e+00	2.58e-03	2.82e-03	2.96e-03	4.09e+00	4.17e+00	3.91e+00	7.78e-04	8.26e-04	6.80e-04
chr13	19749682	19824257	ENSMUSG00000053101	0.3961	-	Gpr141	protein_coding	3.72e-05	3.75e-03	1.46078	4.97e+00	9.62e-03	6.44e+00	2.70e-02	5.13e+00	4.46e+00	5.32e+00	1.04e-02	6.45e-03	1.20e-02	6.88e+00	6.23e+00	6.21e+00	3.59e-02	2.27e-02	2.24e-02
chr4	11758147	11817895	ENSMUSG00000028217	0.4191	+	Cdh17	protein_coding	3.76e-05	3.76e-03	3.78660	5.28e-01	1.54e-04	4.51e+00	5.09e-03	0.00e+00	1.58e+00	0.00e+00	0.00e+00	4.63e-04	0.00e+00	4.46e+00	4.75e+00	4.32e+00	4.86e-03	6.02e-03	4.40e-03
chr16	85793827	85803113	ENSMUSG00000022893	0.4673	-	Adamts1	protein_coding	3.87e-05	3.84e-03	-1.47226	8.67e+00	9.94e-02	7.22e+00	3.57e-02	8.49e+00	8.52e+00	9.01e+00	8.64e-02	8.78e-02	1.24e-01	7.15e+00	7.19e+00	7.32e+00	3.39e-02	3.49e-02	3.82e-02
chr1	82233101	82291416	ENSMUSG00000055980	0.4278	-	Irs1	protein_coding	4.08e-05	4.01e-03	-1.35939	8.67e+00	6.27e-02	7.23e+00	2.43e-02	8.73e+00	8.80e+00	8.48e+00	6.50e-02	6.84e-02	5.46e-02	7.91e+00	7.03e+00	6.75e+00	3.66e-02	1.99e-02	1.64e-02
chr10	23894688	23905343	ENSMUSG00000037440	0.4220	+	Vnn1	protein_coding	4.12e-05	4.02e-03	1.79008	3.94e+00	7.72e-03	5.78e+00	2.80e-02	4.32e+00	3.32e+00	4.17e+00	9.78e-03	4.63e-03	8.75e-03	5.55e+00	5.91e+00	5.88e+00	2.37e-02	3.04e-02	2.99e-02
chr14	55603571	55610030	ENSMUSG00000002325	0.5048	+	Irf9	protein_coding	4.19e-05	4.04e-03	1.41741	4.48e+00	5.11e-03	6.07e+00	1.40e-02	4.64e+00	3.46e+00	5.32e+00	5.04e-03	2.10e-03	8.19e-03	5.81e+00	6.09e+00	6.30e+00	1.16e-02	1.41e-02	1.64e-02
chr7	45335290	45338974	ENSMUSG00000038239	0.5514	+	Hrc	protein_coding	4.24e-05	4.06e-03	2.20163	1.86e+00	2.22e-03	4.80e+00	1.16e-02	0.00e+00	1.58e+00	4.00e+00	0.00e+00	7.82e-04	5.87e-03	3.91e+00	5.46e+00	5.04e+00	5.48e-03	1.68e-02	1.25e-02

Download the DEG result list for Lbil22_vs_Lcil22.
Download the whole result list for Lbil22_vs_Lcil22.

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.