metaseqR report

Summary

Analysis summary

Summary: The raw bam files, one for each RNA-Seq sample, were summarized to a 3'UTR read counts table, using the Bioconductor package GenomicRanges. In the final read counts table, each row represented each column one RNA-Seq sample and each cell, the corresponding read counts associated with each row and column.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 gene s that had zero counts over all the RNA-Seq samples (31322 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/transcripts presenting any of the following were excluded from further analysis: i) genes with length less than 500 (4060 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 average reads per 100 bp), iii) genes with read counts below the median read counts of the total normalized count distribution (4804 genes with cutoff value 1 normalized read counts), iv) genes whose biotype matched the following: rRNA, IG_V_pseudogene, TR_V_pseudogene (105 genes). The total number of genes excluded due to the application of gene filters was 4166. The total (unified) number of genes excluded due to the application of all filters was 38976. The resulting gene counts table was subjected to differential expression analysis for the contrasts APCGFP versus APCunsort, APCTOM versus APCunsort, APCGFP versus APCTOM using the Bioconductor package DESeq. The final numbers of differentially expressed gene s were (per contrast): for the contrast APCGFP versus APCunsort, 1750 (615) statistically significant genes were found with a p-value (FDR or adjusted p-value) threshold of 0.05 and of these, 831 (248) were up-regulated, 919 (367) were down-regulated and 0 (0) were not differentially expressed according to an absolute fold change cutoff value of 1 in log₂ scale, for the contrast APCTOM versus APCunsort, 589 (176) statistically significant genes were found with a p-value (FDR or adjusted p-value) threshold of 0.05 and of these, 86 (27) were up-regulated, 503 (149) were down-regulated and 0 (0) were not differentially expressed according to an absolute fold change cutoff value of 1 in log₂ scale, for the contrast APCGFP versus APCTOM, 1267 (278) statistically significant genes were found with a p-value (FDR or adjusted p-value) threshold of 0.05 and of these, 827 (214) were up-regulated, 440 (64) were down-regulated and 0 (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 sam/bam/bed files
Conditions: APCunsort, APCGFP, APCTOM
Samples included: VK3R16UAPC1, VK3R17UAPC2, VK3R18UAPC3, VK3R19GAPC1, VK3R20GAPC2, VK3R21GAPC3, VK3R22TAPC1, VK3R23TAPC2, VK3R24TAPC3
Samples excluded: none
Requested contrasts: APCGFP_vs_APCunsort, APCTOM_vs_APCunsort, APCGFP_vs_APCTOM
Library sizes:

VK3R16UAPC1: 2649044
VK3R17UAPC2: 2632235
VK3R18UAPC3: 3063999
VK3R19GAPC1: 2540266
VK3R20GAPC2: 2807355
VK3R21GAPC3: 3371679
VK3R22TAPC1: 2267790
VK3R23TAPC2: 2717261
VK3R24TAPC3: 3059244

Annotation: download
Organism: mouse (Mus musculus), genome version alias mm10
Annotation source: Ensembl genomes
Count type: utr
3' UTR flanking :: 250
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: /mnt/max/b/genomics_facility/proton2/run433/wwwVKlab/metaseqR-1.25.1/metaseqr_run433-utr.flank250
Output data: Annotation, p-value, Adjusted p-value (FDR), Fold change, Read counts
Output scale(s): log2 scale, Reads per Gene Model
Output values: Normalized values
Output statistics: Mean, Median, Standard deviation, Median Absolute Deviation (MAD), Coefficient of Variation, Robust Coefficient of Variation
Total run time: 19 minutes 34 seconds

Filtered genes

Number of filtered gene s: 38976 which is the union of

Filtered because of zero reads: 31322
Filtered because of exon filters: 0
Filtered because of gene filters: 7654 which is the union of
- length: 4060 genes with filter cutoff value 500
- avg.reads: 0 genes with filter cutoff value 0
- expression: 4804 genes further decomposed to (filter name, filtered genes, filter cutoff):
- - median: 4804 genes with filter cutoff value 1
- biotype: 105 genes with filter cutoff value rRNA, IG_V_pseudogene, TR_V_pseudogene

Differentially expressed genes

Number of differentially expressed genes per contrast:

APCGFP_vs_APCunsort: 1750 (615) statistically significant genes of which 831 (248) up regulated, 919 (367) down regulated and 0 (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.
APCTOM_vs_APCunsort: 589 (176) statistically significant genes of which 86 (27) up regulated, 503 (149) down regulated and 0 (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.
APCGFP_vs_APCTOM: 1267 (278) statistically significant genes of which 827 (214) up regulated, 440 (64) down regulated and 0 (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

[ 2019-10-02 15:25:35 ] INFO 2019-10-02 15:25:35: Data processing started...
[ 2019-10-02 15:25:35 ] INFO Read counts file: imported sam/bam/bed files
[ 2019-10-02 15:25:35 ] INFO Conditions: APCunsort, APCGFP, APCTOM
[ 2019-10-02 15:25:35 ] INFO Samples to include: VK3R16UAPC1, VK3R17UAPC2, VK3R18UAPC3, VK3R19GAPC1, VK3R20GAPC2, VK3R21GAPC3, VK3R22TAPC1, VK3R23TAPC2, VK3R24TAPC3
[ 2019-10-02 15:25:35 ] INFO Samples to exclude: none
[ 2019-10-02 15:25:35 ] INFO Requested contrasts: APCGFP_vs_APCunsort, APCTOM_vs_APCunsort, APCGFP_vs_APCTOM
[ 2019-10-02 15:25:35 ] INFO Annotation: download
[ 2019-10-02 15:25:35 ] INFO Organism: mm10
[ 2019-10-02 15:25:35 ] INFO Reference source: ensembl
[ 2019-10-02 15:25:35 ] INFO Count type: utr
[ 2019-10-02 15:25:35 ] INFO 3' UTR flanking: 250
[ 2019-10-02 15:25:35 ] INFO Transcriptional level: gene
[ 2019-10-02 15:25:35 ] INFO Exon filters: none applied
[ 2019-10-02 15:25:35 ] INFO Gene filters: length, avg.reads, expression, biotype
[ 2019-10-02 15:25:35 ] INFO length:
[ 2019-10-02 15:25:35 ] INFO length: 500
[ 2019-10-02 15:25:35 ] INFO avg.reads:
[ 2019-10-02 15:25:35 ] INFO average.per.bp: 100
[ 2019-10-02 15:25:35 ] INFO quantile: 0.25
[ 2019-10-02 15:25:35 ] INFO expression:
[ 2019-10-02 15:25:35 ] INFO median: TRUE
[ 2019-10-02 15:25:35 ] INFO mean: FALSE
[ 2019-10-02 15:25:35 ] INFO quantile: NA
[ 2019-10-02 15:25:35 ] INFO known: NA
[ 2019-10-02 15:25:35 ] INFO custom: NA
[ 2019-10-02 15:25:35 ] INFO biotype:
[ 2019-10-02 15:25:35 ] INFO pseudogene: FALSE
[ 2019-10-02 15:25:35 ] INFO snRNA: FALSE
[ 2019-10-02 15:25:35 ] INFO protein_coding: FALSE
[ 2019-10-02 15:25:35 ] INFO antisense: FALSE
[ 2019-10-02 15:25:35 ] INFO miRNA: FALSE
[ 2019-10-02 15:25:35 ] INFO snoRNA: FALSE
[ 2019-10-02 15:25:35 ] INFO lincRNA: FALSE
[ 2019-10-02 15:25:35 ] INFO processed_transcript: FALSE
[ 2019-10-02 15:25:35 ] INFO misc_RNA: FALSE
[ 2019-10-02 15:25:35 ] INFO rRNA: TRUE
[ 2019-10-02 15:25:35 ] INFO sense_intronic: FALSE
[ 2019-10-02 15:25:35 ] INFO sense_overlapping: FALSE
[ 2019-10-02 15:25:35 ] INFO polymorphic_pseudogene: FALSE
[ 2019-10-02 15:25:35 ] INFO IG_C_gene: FALSE
[ 2019-10-02 15:25:35 ] INFO IG_J_gene: FALSE
[ 2019-10-02 15:25:35 ] INFO IG_D_gene: FALSE
[ 2019-10-02 15:25:35 ] INFO IG_LV_gene: FALSE
[ 2019-10-02 15:25:35 ] INFO IG_V_gene: FALSE
[ 2019-10-02 15:25:35 ] INFO IG_V_pseudogene: TRUE
[ 2019-10-02 15:25:35 ] INFO TR_V_gene: FALSE
[ 2019-10-02 15:25:35 ] INFO TR_V_pseudogene: TRUE
[ 2019-10-02 15:25:35 ] INFO three_prime_overlapping_ncrna: FALSE
[ 2019-10-02 15:25:35 ] INFO Filter application: postnorm
[ 2019-10-02 15:25:35 ] INFO Normalization algorithm: deseq
[ 2019-10-02 15:25:35 ] INFO Normalization arguments:
[ 2019-10-02 15:25:35 ] INFO locfunc:
[ 2019-10-02 15:25:35 ] INFO [[
[ 2019-10-02 15:25:35 ] INFO list(locfunc = function (x, na.rm = FALSE) standardGeneric("median"))
[ 2019-10-02 15:25:35 ] INFO locfunc
[ 2019-10-02 15:25:35 ] INFO Statistical algorithm: deseq
[ 2019-10-02 15:25:35 ] INFO Statistical arguments:
[ 2019-10-02 15:25:35 ] INFO deseq: blind, fit-only, local
[ 2019-10-02 15:25:35 ] INFO Meta-analysis method: none
[ 2019-10-02 15:25:35 ] INFO Multiple testing correction: BH
[ 2019-10-02 15:25:35 ] INFO p-value threshold: 0.05
[ 2019-10-02 15:25:35 ] INFO Logarithmic transformation offset: 1
[ 2019-10-02 15:25:35 ] INFO Quality control plots: mds, biodetection, countsbio, saturation, readnoise, filtered, correl, pairwise, boxplot, gcbias, lengthbias, meandiff, meanvar, biodist, volcano, deheatmap
[ 2019-10-02 15:25:35 ] INFO Figure format: png, pdf
[ 2019-10-02 15:25:35 ] INFO Output directory: /mnt/max/b/genomics_facility/proton2/run433/wwwVKlab/metaseqR-1.25.1/metaseqr_run433-utr.flank250
[ 2019-10-02 15:25:35 ] INFO Output data: annotation, p.value, adj.p.value, fold.change, counts, flags
[ 2019-10-02 15:25:35 ] INFO Output scale(s): log2, rpgm
[ 2019-10-02 15:25:35 ] INFO Output values: normalized
[ 2019-10-02 15:25:35 ] INFO Downloading gene annotation for mm10...
[ 2019-10-02 15:26:18 ] INFO Downloading transcript annotation for mm10...
[ 2019-10-02 15:28:54 ] INFO Converting annotation to GenomicRanges object...
[ 2019-10-02 15:28:54 ] INFO Merging transcript 3' UTRs to create unique gene models...
[ 2019-10-02 15:32:40 ] INFO Flanking merged transcript 3' UTRs per 250bp...
[ 2019-10-02 15:32:44 ] INFO Reading bam file VK3R16UAPC1.bam for sample with name VK3R16UAPC1. This might take some time...
[ 2019-10-02 15:32:44 ] INFO Reading bam file VK3R17UAPC2.bam for sample with name VK3R17UAPC2. This might take some time...
[ 2019-10-02 15:32:44 ] INFO Reading bam file VK3R18UAPC3.bam for sample with name VK3R18UAPC3. This might take some time...
[ 2019-10-02 15:32:44 ] INFO Reading bam file VK3R19GAPC1.bam for sample with name VK3R19GAPC1. This might take some time...
[ 2019-10-02 15:32:44 ] INFO Reading bam file VK3R20GAPC2.bam for sample with name VK3R20GAPC2. This might take some time...
[ 2019-10-02 15:32:44 ] INFO Reading bam file VK3R21GAPC3.bam for sample with name VK3R21GAPC3. This might take some time...
[ 2019-10-02 15:32:44 ] INFO Reading bam file VK3R22TAPC1.bam for sample with name VK3R22TAPC1. This might take some time...
[ 2019-10-02 15:32:44 ] INFO Reading bam file VK3R23TAPC2.bam for sample with name VK3R23TAPC2. This might take some time...
[ 2019-10-02 15:32:44 ] INFO Reading bam file VK3R24TAPC3.bam for sample with name VK3R24TAPC3. This might take some time...
[ 2019-10-02 15:32:48 ] INFO Counting reads overlapping with given annotation...
[ 2019-10-02 15:32:48 ] INFO ...for single-end reads...
[ 2019-10-02 15:32:48 ] INFO ...assuming forward sequenced reads...
[ 2019-10-02 15:32:48 ] INFO Counting reads overlapping with given annotation...
[ 2019-10-02 15:32:48 ] INFO ...for single-end reads...
[ 2019-10-02 15:32:48 ] INFO ...assuming forward sequenced reads...
[ 2019-10-02 15:32:49 ] INFO Counting reads overlapping with given annotation...
[ 2019-10-02 15:32:49 ] INFO ...for single-end reads...
[ 2019-10-02 15:32:49 ] INFO ...assuming forward sequenced reads...
[ 2019-10-02 15:32:49 ] INFO Counting reads overlapping with given annotation...
[ 2019-10-02 15:32:49 ] INFO ...for single-end reads...
[ 2019-10-02 15:32:49 ] INFO ...assuming forward sequenced reads...
[ 2019-10-02 15:32:49 ] INFO Counting reads overlapping with given annotation...
[ 2019-10-02 15:32:49 ] INFO ...for single-end reads...
[ 2019-10-02 15:32:49 ] INFO ...assuming forward sequenced reads...
[ 2019-10-02 15:32:49 ] INFO Counting reads overlapping with given annotation...
[ 2019-10-02 15:32:49 ] INFO ...for single-end reads...
[ 2019-10-02 15:32:49 ] INFO ...assuming forward sequenced reads...
[ 2019-10-02 15:32:49 ] INFO Counting reads overlapping with given annotation...
[ 2019-10-02 15:32:49 ] INFO ...for single-end reads...
[ 2019-10-02 15:32:49 ] INFO ...assuming forward sequenced reads...
[ 2019-10-02 15:32:49 ] INFO Counting reads overlapping with given annotation...
[ 2019-10-02 15:32:49 ] INFO ...for single-end reads...
[ 2019-10-02 15:32:49 ] INFO ...assuming forward sequenced reads...
[ 2019-10-02 15:32:50 ] INFO Counting reads overlapping with given annotation...
[ 2019-10-02 15:32:50 ] INFO ...for single-end reads...
[ 2019-10-02 15:32:50 ] INFO ...assuming forward sequenced reads...
[ 2019-10-02 15:33:09 ] INFO Exporting raw read counts table to /mnt/max/b/genomics_facility/proton2/run433/wwwVKlab/metaseqR-1.25.1/metaseqr_run433-utr.flank250/lists/raw_counts_table.txt.gz
[ 2019-10-02 15:33:12 ] INFO Checking chromosomes in transcript counts and gene annotation...
[ 2019-10-02 15:33:12 ] INFO Processing transcripts...
[ 2019-10-02 15:33:12 ] INFO Separating transcripts (UTR regions) per for VK3R16UAPC1...
[ 2019-10-02 15:33:35 ] INFO Separating transcripts (UTR regions) per for VK3R17UAPC2...
[ 2019-10-02 15:34:06 ] INFO Separating transcripts (UTR regions) per for VK3R18UAPC3...
[ 2019-10-02 15:34:34 ] INFO Separating transcripts (UTR regions) per for VK3R19GAPC1...
[ 2019-10-02 15:35:02 ] INFO Separating transcripts (UTR regions) per for VK3R20GAPC2...
[ 2019-10-02 15:35:30 ] INFO Separating transcripts (UTR regions) per for VK3R21GAPC3...
[ 2019-10-02 15:36:03 ] INFO Separating transcripts (UTR regions) per for VK3R22TAPC1...
[ 2019-10-02 15:36:35 ] INFO Separating transcripts (UTR regions) per for VK3R23TAPC2...
[ 2019-10-02 15:37:08 ] INFO Separating transcripts (UTR regions) per for VK3R24TAPC3...
[ 2019-10-02 15:37:40 ] INFO Saving gene model to /mnt/max/b/genomics_facility/proton2/run433/wwwVKlab/metaseqR-1.25.1/metaseqr_run433-utr.flank250/data/gene_model.RData
[ 2019-10-02 15:37:45 ] INFO Summarizing count data...
[ 2019-10-02 15:38:15 ] INFO Removing genes with zero counts in all samples...
[ 2019-10-02 15:38:16 ] INFO Normalizing with: deseq
[ 2019-10-02 15:38:16 ] INFO Applying gene filter length...
[ 2019-10-02 15:38:16 ] INFO Threshold below which ignored: 500
[ 2019-10-02 15:38:16 ] INFO Applying gene filter avg.reads...
[ 2019-10-02 15:38:16 ] INFO Threshold below which ignored: 0
[ 2019-10-02 15:38:16 ] INFO Applying gene filter expression...
[ 2019-10-02 15:38:16 ] INFO Threshold below which ignored: 1
[ 2019-10-02 15:38:16 ] INFO Applying gene filter biotype...
[ 2019-10-02 15:38:16 ] INFO Biotypes ignored: rRNA, IG_V_pseudogene, TR_V_pseudogene
[ 2019-10-02 15:38:16 ] INFO 38976 genes filtered out
[ 2019-10-02 15:38:16 ] INFO 16322 genes remain after filtering
[ 2019-10-02 15:38:16 ] INFO Running statistical tests with: deseq
[ 2019-10-02 15:38:16 ] INFO Contrast: APCGFP_vs_APCunsort
[ 2019-10-02 15:38:22 ] INFO Contrast: APCTOM_vs_APCunsort
[ 2019-10-02 15:38:26 ] INFO Contrast: APCGFP_vs_APCTOM
[ 2019-10-02 15:38:31 ] INFO Contrast APCGFP_vs_APCunsort: found 1750 genes
[ 2019-10-02 15:38:31 ] INFO Contrast APCTOM_vs_APCunsort: found 589 genes
[ 2019-10-02 15:38:31 ] INFO Contrast APCGFP_vs_APCTOM: found 1267 genes
[ 2019-10-02 15:38:31 ] INFO Exporting and compressing normalized read counts table to /mnt/max/b/genomics_facility/proton2/run433/wwwVKlab/metaseqR-1.25.1/metaseqr_run433-utr.flank250/lists/normalized_counts_table.txt
[ 2019-10-02 15:38:33 ] INFO Building output files...
[ 2019-10-02 15:38:37 ] INFO Contrast: APCGFP_vs_APCunsort
[ 2019-10-02 15:38:37 ] INFO Adding non-filtered data...
[ 2019-10-02 15:38:37 ] INFO binding annotation...
[ 2019-10-02 15:38:38 ] INFO binding p-values...
[ 2019-10-02 15:38:38 ] INFO binding FDRs...
[ 2019-10-02 15:38:38 ] INFO binding log2 normalized fold changes...
[ 2019-10-02 15:38:39 ] INFO binding all normalized counts for APCGFP...
[ 2019-10-02 15:38:39 ] INFO binding all normalized counts for APCunsort...
[ 2019-10-02 15:38:40 ] INFO binding filtering flags...
[ 2019-10-02 15:38:41 ] INFO Writing output...
[ 2019-10-02 15:38:41 ] INFO Adding filtered data...
[ 2019-10-02 15:38:41 ] INFO binding annotation...
[ 2019-10-02 15:38:41 ] INFO binding p-values...
[ 2019-10-02 15:38:41 ] INFO binding FDRs...
[ 2019-10-02 15:38:41 ] INFO binding log2 normalized fold changes...
[ 2019-10-02 15:38:41 ] INFO binding all normalized counts for APCGFP...
[ 2019-10-02 15:38:41 ] INFO binding all normalized counts for APCunsort...
[ 2019-10-02 15:38:41 ] INFO binding filtering flags...
[ 2019-10-02 15:38:42 ] INFO Writing output...
[ 2019-10-02 15:38:44 ] INFO Contrast: APCTOM_vs_APCunsort
[ 2019-10-02 15:38:44 ] INFO Adding non-filtered data...
[ 2019-10-02 15:38:44 ] INFO binding annotation...
[ 2019-10-02 15:38:45 ] INFO binding p-values...
[ 2019-10-02 15:38:45 ] INFO binding FDRs...
[ 2019-10-02 15:38:45 ] INFO binding log2 normalized fold changes...
[ 2019-10-02 15:38:45 ] INFO binding all normalized counts for APCTOM...
[ 2019-10-02 15:38:46 ] INFO binding all normalized counts for APCunsort...
[ 2019-10-02 15:38:46 ] INFO binding filtering flags...
[ 2019-10-02 15:38:47 ] INFO Writing output...
[ 2019-10-02 15:38:47 ] INFO Adding filtered data...
[ 2019-10-02 15:38:47 ] INFO binding annotation...
[ 2019-10-02 15:38:47 ] INFO binding p-values...
[ 2019-10-02 15:38:47 ] INFO binding FDRs...
[ 2019-10-02 15:38:48 ] INFO binding log2 normalized fold changes...
[ 2019-10-02 15:38:48 ] INFO binding all normalized counts for APCTOM...
[ 2019-10-02 15:38:48 ] INFO binding all normalized counts for APCunsort...
[ 2019-10-02 15:38:48 ] INFO binding filtering flags...
[ 2019-10-02 15:38:49 ] INFO Writing output...
[ 2019-10-02 15:38:51 ] INFO Contrast: APCGFP_vs_APCTOM
[ 2019-10-02 15:38:51 ] INFO Adding non-filtered data...
[ 2019-10-02 15:38:51 ] INFO binding annotation...
[ 2019-10-02 15:38:51 ] INFO binding p-values...
[ 2019-10-02 15:38:51 ] INFO binding FDRs...
[ 2019-10-02 15:38:52 ] INFO binding log2 normalized fold changes...
[ 2019-10-02 15:38:52 ] INFO binding all normalized counts for APCGFP...
[ 2019-10-02 15:38:53 ] INFO binding all normalized counts for APCTOM...
[ 2019-10-02 15:38:53 ] INFO binding filtering flags...
[ 2019-10-02 15:38:54 ] INFO Writing output...
[ 2019-10-02 15:38:54 ] INFO Adding filtered data...
[ 2019-10-02 15:38:54 ] INFO binding annotation...
[ 2019-10-02 15:38:54 ] INFO binding p-values...
[ 2019-10-02 15:38:54 ] INFO binding FDRs...
[ 2019-10-02 15:38:55 ] INFO binding log2 normalized fold changes...
[ 2019-10-02 15:38:55 ] INFO binding all normalized counts for APCGFP...
[ 2019-10-02 15:38:55 ] INFO binding all normalized counts for APCTOM...
[ 2019-10-02 15:38:55 ] INFO binding filtering flags...
[ 2019-10-02 15:38:56 ] INFO Writing output...
[ 2019-10-02 15:38:58 ] INFO Creating quality control graphs...
[ 2019-10-02 15:38:58 ] INFO Plotting in png format...
[ 2019-10-02 15:38:58 ] INFO Plotting mds...
[ 2019-10-02 15:38:58 ] INFO Plotting biodetection...
[ 2019-10-02 15:38:59 ] INFO Plotting countsbio...
[ 2019-10-02 15:39:01 ] INFO Plotting saturation...
[ 2019-10-02 15:39:07 ] INFO Plotting readnoise...
[ 2019-10-02 15:39:08 ] INFO Plotting correl...
[ 2019-10-02 15:39:08 ] INFO Plotting pairwise...
[ 2019-10-02 15:39:33 ] INFO Plotting boxplot...
[ 2019-10-02 15:39:34 ] INFO Plotting gcbias...
[ 2019-10-02 15:39:35 ] INFO Plotting lengthbias...
[ 2019-10-02 15:39:35 ] INFO Plotting meandiff...
[ 2019-10-02 15:39:37 ] INFO Plotting meanvar...
[ 2019-10-02 15:39:47 ] INFO Plotting boxplot...
[ 2019-10-02 15:39:48 ] INFO Plotting gcbias...
[ 2019-10-02 15:39:49 ] INFO Plotting lengthbias...
[ 2019-10-02 15:39:49 ] INFO Plotting meandiff...
[ 2019-10-02 15:39:51 ] INFO Plotting meanvar...
[ 2019-10-02 15:40:01 ] INFO Plotting biodist...
[ 2019-10-02 15:40:01 ] INFO Contrast: APCGFP_vs_APCunsort
[ 2019-10-02 15:40:02 ] INFO Contrast: APCTOM_vs_APCunsort
[ 2019-10-02 15:40:02 ] INFO Contrast: APCGFP_vs_APCTOM
[ 2019-10-02 15:40:03 ] INFO Plotting volcano...
[ 2019-10-02 15:40:03 ] INFO Contrast: APCGFP_vs_APCunsort
[ 2019-10-02 15:40:03 ] INFO Contrast: APCTOM_vs_APCunsort
[ 2019-10-02 15:40:04 ] INFO Contrast: APCGFP_vs_APCTOM
[ 2019-10-02 15:40:04 ] INFO Plotting deheatmap...
[ 2019-10-02 15:40:04 ] INFO Contrast: APCGFP_vs_APCunsort
[ 2019-10-02 15:40:46 ] INFO Contrast: APCTOM_vs_APCunsort
[ 2019-10-02 15:41:28 ] INFO Contrast: APCGFP_vs_APCTOM
[ 2019-10-02 15:42:09 ] INFO Plotting filtered...
[ 2019-10-02 15:42:09 ] INFO Plotting in pdf format...
[ 2019-10-02 15:42:09 ] INFO Plotting mds...
[ 2019-10-02 15:42:09 ] INFO Plotting biodetection...
[ 2019-10-02 15:42:10 ] INFO Plotting countsbio...
[ 2019-10-02 15:42:11 ] INFO Plotting saturation...
[ 2019-10-02 15:42:16 ] INFO Plotting readnoise...
[ 2019-10-02 15:42:17 ] INFO Plotting correl...
[ 2019-10-02 15:42:17 ] INFO Plotting pairwise...
[ 2019-10-02 15:42:31 ] INFO Plotting boxplot...
[ 2019-10-02 15:42:31 ] INFO Plotting gcbias...
[ 2019-10-02 15:42:32 ] INFO Plotting lengthbias...
[ 2019-10-02 15:42:33 ] INFO Plotting meandiff...
[ 2019-10-02 15:42:35 ] INFO Plotting meanvar...
[ 2019-10-02 15:42:45 ] INFO Plotting boxplot...
[ 2019-10-02 15:42:45 ] INFO Plotting gcbias...
[ 2019-10-02 15:42:46 ] INFO Plotting lengthbias...
[ 2019-10-02 15:42:47 ] INFO Plotting meandiff...
[ 2019-10-02 15:42:49 ] INFO Plotting meanvar...
[ 2019-10-02 15:42:59 ] INFO Plotting biodist...
[ 2019-10-02 15:42:59 ] INFO Contrast: APCGFP_vs_APCunsort
[ 2019-10-02 15:42:59 ] INFO Contrast: APCTOM_vs_APCunsort
[ 2019-10-02 15:42:59 ] INFO Contrast: APCGFP_vs_APCTOM
[ 2019-10-02 15:42:59 ] INFO Plotting volcano...
[ 2019-10-02 15:42:59 ] INFO Contrast: APCGFP_vs_APCunsort
[ 2019-10-02 15:43:00 ] INFO Contrast: APCTOM_vs_APCunsort
[ 2019-10-02 15:43:00 ] INFO Contrast: APCGFP_vs_APCTOM
[ 2019-10-02 15:43:00 ] INFO Plotting deheatmap...
[ 2019-10-02 15:43:00 ] INFO Contrast: APCGFP_vs_APCunsort
[ 2019-10-02 15:43:43 ] INFO Contrast: APCTOM_vs_APCunsort
[ 2019-10-02 15:44:24 ] INFO Contrast: APCGFP_vs_APCTOM
[ 2019-10-02 15:45:07 ] INFO Plotting filtered...
[ 2019-10-02 15:45:07 ] INFO Creating HTML report...
[ 2019-10-02 15:45:07 ] 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 APCunsort

Mean-difference plots for the replicates of APCGFP

Mean-difference plots for the replicates of APCTOM

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 APCunsort

Mean-variance plots for the replicates of APCGFP

Mean-variance plots for the replicates of APCTOM

Chromosome and biotype distribution of filtered gene s

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 APCGFP_vs_APCunsort

Volcano plot for the contrast APCTOM_vs_APCunsort

Volcano plot for the contrast APCGFP_vs_APCTOM

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 APCGFP_vs_APCunsort

DEG heatmap for the contrast APCTOM_vs_APCunsort

DEG heatmap for the contrast APCGFP_vs_APCTOM

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 APCGFP_vs_APCunsort

Biotype distribution in DEG for the contrast APCTOM_vs_APCunsort

Biotype distribution in DEG for the contrast APCGFP_vs_APCTOM

Get all the figures in png, pdf format.

Results

DEG table for the contrast APCGFP_vs_APCunsort

The following table presents the top 5% statistically significant genes (use the download links below the table to retrieve the whole list) for the contrast APCGFP_vs_APCunsort. 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_APCGFP_vs_APCunsort	log2_normalized_counts_VK3R19GAPC1	log2_normalized_counts_VK3R20GAPC2	log2_normalized_counts_VK3R21GAPC3	rpgm_normalized_counts_VK3R19GAPC1	rpgm_normalized_counts_VK3R20GAPC2	rpgm_normalized_counts_VK3R21GAPC3	log2_normalized_counts_VK3R16UAPC1	log2_normalized_counts_VK3R17UAPC2	log2_normalized_counts_VK3R18UAPC3	rpgm_normalized_counts_VK3R16UAPC1	rpgm_normalized_counts_VK3R17UAPC2	rpgm_normalized_counts_VK3R18UAPC3
chr8	21593375	21594508	ENSMUSG00000079113	42.50	-	Gm7861	protein_coding	4.28e-16	6.98e-12	8.59619	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	8.21e+00	5.17e+00	9.70e+00	5.91e-01	7.01e-02	1.66e+00
chr8	21191614	21192549	ENSMUSG00000074442	42.95	+	Defa31	protein_coding	3.59e-14	2.14e-10	7.93467	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	7.62e+00	4.58e+00	9.00e+00	3.93e-01	4.61e-02	1.03e+00
chr8	21655780	21656735	ENSMUSG00000060208	44.04	+	Defa17	protein_coding	3.93e-14	2.14e-10	7.69792	3.00e+00	3.00e+00	2.58e+00	1.40e-02	1.40e-02	1.00e-02	1.05e+01	8.78e+00	1.14e+01	2.83e+00	8.82e-01	5.44e+00
chr1	169655501	169695813	ENSMUSG00000026678	41.35	+	Rgs5	protein_coding	6.14e-14	2.50e-10	-7.47199	9.35e+00	9.27e+00	1.00e+01	6.51e-01	6.17e-01	1.04e+00	1.58e+00	1.00e+00	3.00e+00	2.00e-03	1.00e-03	7.01e-03
chr9	123150946	123157432	ENSMUSG00000025784	50.01	+	Clec3b	protein_coding	2.25e-13	7.33e-10	-7.39120	1.12e+01	8.90e+00	9.96e+00	4.78e+00	9.54e-01	2.00e+00	3.58e+00	2.58e+00	2.32e+00	2.20e-02	1.00e-02	8.02e-03
chr8	21455445	21456560	ENSMUSG00000079114	42.65	-	Gm7849	protein_coding	1.03e-12	2.80e-09	7.21723	0.00e+00	0.00e+00	1.58e+00	0.00e+00	0.00e+00	4.01e-03	7.38e+00	4.17e+00	9.13e+00	3.31e-01	3.41e-02	1.12e+00
chr8	21162277	21163249	ENSMUSG00000074443	43.78	+	Defa22	protein_coding	1.80e-12	4.19e-09	7.34577	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	7.68e+00	4.52e+00	8.02e+00	4.09e-01	4.41e-02	5.19e-01
chr1	45374321	45503282	ENSMUSG00000026042	36.19	-	Col5a2	protein_coding	3.49e-12	6.56e-09	-6.70936	9.92e+00	1.03e+01	1.00e+01	4.85e-01	6.13e-01	5.25e-01	3.00e+00	3.91e+00	3.00e+00	3.51e-03	7.01e-03	3.51e-03
chr17	31141049	31144282	ENSMUSG00000024028	50.15	-	Tff2	protein_coding	3.62e-12	6.56e-09	6.80521	0.00e+00	1.00e+00	1.58e+00	0.00e+00	2.00e-03	4.01e-03	7.29e+00	7.17e+00	8.53e+00	3.13e-01	2.87e-01	7.39e-01
chr8	21427411	21428391	ENSMUSG00000094662	41.39	+	Defa36	protein_coding	4.51e-12	7.36e-09	6.87213	0.00e+00	1.58e+00	1.58e+00	0.00e+00	4.01e-03	4.01e-03	8.32e+00	5.67e+00	8.81e+00	6.39e-01	1.00e-01	8.98e-01
chr8	21565386	21566349	ENSMUSG00000065956	41.18	+	Defa37	protein_coding	1.07e-11	1.54e-08	6.65561	0.00e+00	2.58e+00	2.00e+00	0.00e+00	1.00e-02	6.01e-03	8.77e+00	5.98e+00	9.25e+00	8.72e-01	1.24e-01	1.22e+00
chr8	21025545	21026516	ENSMUSG00000074447	44.14	+	Defa21	protein_coding	1.14e-11	1.54e-08	6.95420	1.00e+00	0.00e+00	0.00e+00	2.00e-03	0.00e+00	0.00e+00	7.52e+00	5.13e+00	8.12e+00	3.65e-01	6.81e-02	5.55e-01
chr8	21055047	21056016	ENSMUSG00000074446	43.40	+	Defa23	protein_coding	1.24e-11	1.54e-08	6.96000	1.00e+00	0.00e+00	0.00e+00	2.00e-03	0.00e+00	0.00e+00	7.01e+00	4.64e+00	8.43e+00	2.57e-01	4.81e-02	6.87e-01
chr12	109541001	109571726	ENSMUSG00000021268	50.22	+	Meg3	lncRNA	1.34e-11	1.54e-08	-6.41414	9.69e+00	9.53e+00	9.14e+00	2.07e-01	1.85e-01	1.41e-01	3.81e+00	2.32e+00	2.58e+00	3.26e-03	1.00e-03	1.25e-03
chr8	21665797	21666732	ENSMUSG00000063206	40.71	+	Defa34	protein_coding	1.42e-11	1.54e-08	6.95420	0.00e+00	0.00e+00	1.00e+00	0.00e+00	0.00e+00	2.00e-03	7.44e+00	5.78e+00	8.06e+00	3.47e-01	1.08e-01	5.33e-01
chr19	5068078	5070682	ENSMUSG00000056481	61.27	+	Cd248	protein_coding	1.87e-11	1.91e-08	-6.41423	9.80e+00	8.88e+00	8.74e+00	1.79e+00	9.42e-01	8.54e-01	3.32e+00	1.58e+00	3.00e+00	1.80e-02	4.01e-03	1.40e-02
chr11	102835849	102838040	ENSMUSG00000020928	51.92	+	Higd1b	protein_coding	2.09e-11	2.01e-08	-6.66534	7.89e+00	7.38e+00	7.68e+00	4.75e-01	3.31e-01	4.09e-01	1.00e+00	1.00e+00	1.00e+00	2.00e-03	2.00e-03	2.00e-03
chr6	48569696	48572789	ENSMUSG00000009281	50.55	-	Rarres2	protein_coding	2.89e-11	2.62e-08	-6.34988	9.47e+00	9.09e+00	9.27e+00	7.11e-01	5.46e-01	6.19e-01	0.00e+00	2.81e+00	3.91e+00	0.00e+00	6.01e-03	1.40e-02
chr6	4674350	4747207	ENSMUSG00000004631	39.94	-	Sgce	protein_coding	3.12e-11	2.68e-08	-6.63807	7.30e+00	7.54e+00	7.27e+00	2.62e-02	3.09e-02	2.56e-02	1.00e+00	1.00e+00	0.00e+00	1.67e-04	1.67e-04	0.00e+00
chr8	21378515	21379495	ENSMUSG00000096295	44.55	+	Defa2	protein_coding	3.66e-11	2.90e-08	6.13049	2.81e+00	3.17e+00	0.00e+00	1.20e-02	1.60e-02	0.00e+00	8.54e+00	5.52e+00	9.59e+00	7.45e-01	9.02e-02	1.55e+00
chr8	21734494	21735471	ENSMUSG00000064213	43.25	+	Defa24	protein_coding	3.74e-11	2.90e-08	6.40278	2.81e+00	4.70e+00	4.39e+00	1.20e-02	5.01e-02	4.01e-02	1.04e+01	8.76e+00	1.15e+01	2.64e+00	8.68e-01	5.65e+00
chr3	145650312	145677580	ENSMUSG00000097183	43.56	+	Gm17501	lncRNA	4.11e-11	3.05e-08	-6.19605	9.18e+00	8.75e+00	8.57e+00	5.80e-01	4.32e-01	3.81e-01	3.00e+00	3.00e+00	1.58e+00	7.01e-03	7.01e-03	2.00e-03
chr1	193221634	193264075	ENSMUSG00000016194	43.94	-	Hsd11b1	protein_coding	5.27e-11	3.74e-08	-6.17203	9.06e+00	8.57e+00	8.83e+00	2.13e-01	1.52e-01	1.82e-01	2.81e+00	1.58e+00	3.17e+00	2.40e-03	8.02e-04	3.21e-03
chr8	21529032	21530012	ENSMUSG00000094818	44.55	+	Defa32	protein_coding	5.70e-11	3.84e-08	6.31470	0.00e+00	3.00e+00	0.00e+00	0.00e+00	1.40e-02	0.00e+00	8.28e+00	5.78e+00	8.75e+00	6.21e-01	1.08e-01	8.60e-01
chr8	21391811	21392791	ENSMUSG00000094362	44.65	+	Defa33	protein_coding	5.90e-11	3.84e-08	6.54174	0.00e+00	2.00e+00	0.00e+00	0.00e+00	6.01e-03	0.00e+00	7.79e+00	5.64e+00	8.17e+00	4.41e-01	9.82e-02	5.75e-01
chr2	152754173	152765037	ENSMUSG00000009876	50.11	+	Cox4i2	protein_coding	6.12e-11	3.84e-08	-6.58195	7.50e+00	7.16e+00	7.28e+00	3.61e-01	2.85e-01	3.09e-01	1.00e+00	0.00e+00	1.00e+00	2.00e-03	0.00e+00	2.00e-03
chr8	21134642	21135598	ENSMUSG00000074444	43.16	+	Defa30	protein_coding	6.47e-11	3.91e-08	6.14444	3.46e+00	1.00e+00	3.32e+00	2.00e-02	2.00e-03	1.80e-02	9.13e+00	6.61e+00	9.92e+00	1.12e+00	1.94e-01	1.94e+00
chr13	113093825	113100981	ENSMUSG00000023132	43.89	-	Gzma	protein_coding	7.75e-11	4.45e-08	6.16189	1.58e+00	0.00e+00	0.00e+00	4.01e-03	0.00e+00	0.00e+00	6.83e+00	5.55e+00	7.62e+00	2.26e-01	9.22e-02	3.93e-01
chr8	21509258	21510237	ENSMUSG00000095066	44.59	+	Defa20	protein_coding	7.91e-11	4.45e-08	5.99312	3.32e+00	3.32e+00	1.58e+00	1.80e-02	1.80e-02	4.01e-03	8.85e+00	5.49e+00	9.90e+00	9.24e-01	8.82e-02	1.92e+00
chr1	171518122	171535294	ENSMUSG00000038209	42.50	-	Itln1	protein_coding	1.17e-10	6.39e-08	5.90689	3.32e+00	2.81e+00	2.32e+00	9.02e-03	6.01e-03	4.01e-03	8.56e+00	7.40e+00	9.59e+00	3.78e-01	1.68e-01	7.74e-01
chr8	21095074	21096050	ENSMUSG00000061958	42.89	-	Gm14851	protein_coding	1.32e-10	6.97e-08	6.69581	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	6.00e+00	2.00e+00	7.92e+00	1.26e-01	6.01e-03	4.85e-01
chr10	97479609	97518143	ENSMUSG00000019929	37.42	+	Dcn	protein_coding	3.45e-10	1.76e-07	-5.84229	1.01e+01	9.87e+00	9.81e+00	7.29e-01	6.23e-01	6.00e-01	4.00e+00	4.39e+00	3.81e+00	1.00e-02	1.34e-02	8.68e-03
chr8	21702519	21703645	ENSMUSG00000058618	42.95	-	AY761184	protein_coding	4.24e-10	2.04e-07	6.52356	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	5.73e+00	3.00e+00	7.75e+00	1.04e-01	1.40e-02	4.29e-01
chr8	21287409	21288377	ENSMUSG00000074440	44.27	+	Defa3	protein_coding	4.26e-10	2.04e-07	6.31832	0.00e+00	1.00e+00	1.00e+00	0.00e+00	2.00e-03	2.00e-03	6.95e+00	4.39e+00	7.99e+00	2.46e-01	4.01e-02	5.07e-01
chr11	50602084	50807573	ENSMUSG00000036545	48.68	+	Adamts2	protein_coding	4.50e-10	2.04e-07	-5.80149	8.88e+00	8.39e+00	8.71e+00	2.36e-01	1.68e-01	2.09e-01	2.32e+00	3.32e+00	2.81e+00	2.00e-03	4.51e-03	3.01e-03
chr11	78499091	78502324	ENSMUSG00000017344	53.06	+	Vtn	protein_coding	4.51e-10	2.04e-07	-6.35315	6.34e+00	6.63e+00	7.20e+00	5.34e-02	6.55e-02	9.75e-02	0.00e+00	1.00e+00	0.00e+00	0.00e+00	6.68e-04	0.00e+00
chr6	87383256	87388935	ENSMUSG00000030048	48.79	-	Gkn3	protein_coding	5.19e-10	2.29e-07	5.87586	5.49e+00	1.00e+00	4.17e+00	8.82e-02	2.00e-03	3.41e-02	1.11e+01	8.50e+00	1.03e+01	4.40e+00	7.25e-01	2.52e+00
chr2	93955810	93957201	ENSMUSG00000087006	66.09	-	Gm13889	protein_coding	6.06e-10	2.60e-07	-5.87447	7.35e+00	8.08e+00	8.08e+00	3.25e-01	5.41e-01	5.39e-01	0.00e+00	2.32e+00	2.58e+00	0.00e+00	8.02e-03	1.00e-02
chr11	82035571	82037453	ENSMUSG00000035385	43.76	+	Ccl2	protein_coding	1.25e-09	5.24e-07	-5.41247	9.03e+00	9.57e+00	9.21e+00	1.05e+00	1.52e+00	1.18e+00	3.46e+00	4.91e+00	1.58e+00	2.00e-02	5.81e-02	4.01e-03
chr1	172271709	172298064	ENSMUSG00000007097	49.67	-	Atp1a2	protein_coding	1.36e-09	5.54e-07	-5.82704	8.38e+00	7.64e+00	7.68e+00	1.67e-01	9.92e-02	1.02e-01	2.32e+00	0.00e+00	2.81e+00	2.00e-03	0.00e+00	3.01e-03
chr2	164562413	164568510	ENSMUSG00000017723	52.02	+	Wfdc2	protein_coding	1.73e-09	6.88e-07	5.95420	0.00e+00	2.00e+00	0.00e+00	0.00e+00	1.50e-03	0.00e+00	7.46e+00	6.71e+00	6.51e+00	8.77e-02	5.21e-02	4.51e-02
chr6	70726435	70726966	ENSMUSG00000076609	49.25	+	Igkc	IG_C_gene	2.00e-09	7.79e-07	6.57438	3.32e+00	5.17e+00	6.46e+00	1.80e-02	7.01e-02	1.74e-01	1.16e+01	1.07e+01	1.29e+01	6.44e+00	3.33e+00	1.58e+01
chr19	6399340	6415216	ENSMUSG00000032946	53.39	+	Rasgrp2	protein_coding	2.09e-09	7.94e-07	-5.55192	8.77e+00	8.37e+00	8.28e+00	6.26e-02	4.72e-02	4.42e-02	3.32e+00	1.58e+00	3.32e+00	1.29e-03	2.86e-04	1.29e-03
chr14	46383520	46390669	ENSMUSG00000021835	53.05	-	Bmp4	protein_coding	2.21e-09	8.20e-07	-5.44948	9.55e+00	9.65e+00	8.34e+00	5.00e-01	5.36e-01	2.16e-01	2.00e+00	4.86e+00	3.32e+00	2.00e-03	1.87e-02	6.01e-03
chr15	96994820	97055956	ENSMUSG00000022464	44.24	-	Slc38a4	protein_coding	2.76e-09	9.93e-07	5.73245	1.58e+00	1.58e+00	3.58e+00	2.00e-03	2.00e-03	1.10e-02	9.28e+00	6.98e+00	7.70e+00	6.23e-01	1.25e-01	2.07e-01
chr5	137605103	137613784	ENSMUSG00000029718	55.15	-	Pcolce	protein_coding	2.80e-09	9.93e-07	-5.51987	7.99e+00	8.45e+00	7.45e+00	1.27e-01	1.75e-01	8.72e-02	2.81e+00	2.58e+00	2.00e+00	3.01e-03	2.51e-03	1.50e-03
chr14	26027282	26042711	ENSMUSG00000094800	47.95	-	Gm9780	protein_coding	2.87e-09	9.98e-07	-5.40416	8.15e+00	8.72e+00	8.63e+00	2.85e-01	4.21e-01	3.95e-01	3.00e+00	3.32e+00	3.00e+00	7.01e-03	9.02e-03	7.01e-03
chr4	134082448	134095082	ENSMUSG00000000682	49.53	-	Cd52	protein_coding	3.00e-09	1.02e-06	5.88874	0.00e+00	0.00e+00	1.00e+00	0.00e+00	0.00e+00	6.68e-04	6.63e+00	4.81e+00	6.78e+00	6.55e-02	1.80e-02	7.28e-02
chr12	113254830	113260236	ENSMUSG00000095079	51.30	-	Igha	IG_C_gene	3.18e-09	1.06e-06	6.49722	1.58e+00	5.04e+00	6.70e+00	2.00e-03	3.21e-02	1.03e-01	1.25e+01	1.08e+01	1.23e+01	5.97e+00	1.75e+00	4.95e+00
chr10	57784881	57788450	ENSMUSG00000019874	38.38	+	Fabp7	protein_coding	3.56e-09	1.15e-06	-5.94953	6.38e+00	6.86e+00	6.78e+00	8.22e-02	1.15e-01	1.09e-01	0.00e+00	1.58e+00	0.00e+00	0.00e+00	2.00e-03	0.00e+00
chr1	170644532	170682789	ENSMUSG00000038463	49.15	+	Olfml2b	protein_coding	3.61e-09	1.15e-06	-5.55459	7.42e+00	7.01e+00	7.76e+00	3.41e-01	2.57e-01	4.33e-01	2.00e+00	2.00e+00	1.58e+00	6.01e-03	6.01e-03	4.01e-03
chr11	83709015	83711348	ENSMUSG00000000983	45.84	+	Wfdc18	protein_coding	3.87e-09	1.21e-06	5.43664	4.00e+00	4.52e+00	4.25e+00	1.00e-02	1.47e-02	1.20e-02	1.05e+01	8.13e+00	9.62e+00	9.65e-01	1.87e-01	5.24e-01
chr10	75589340	75617200	ENSMUSG00000006344	47.45	+	Ggt5	protein_coding	4.92e-09	1.51e-06	-6.00000	6.00e+00	6.87e+00	6.23e+00	1.80e-02	3.32e-02	2.12e-02	0.00e+00	1.00e+00	0.00e+00	0.00e+00	2.86e-04	0.00e+00
chr9	7502352	7510240	ENSMUSG00000047562	40.35	+	Mmp10	protein_coding	5.12e-09	1.55e-06	-5.47390	1.08e+01	1.13e+01	1.07e+01	3.63e+00	4.96e+00	3.25e+00	4.86e+00	5.98e+00	5.36e+00	5.61e-02	1.24e-01	8.02e-02
chr6	4003904	4008445	ENSMUSG00000032766	43.17	+	Gng11	protein_coding	7.32e-09	2.17e-06	-5.10462	9.02e+00	9.34e+00	9.75e+00	1.04e+00	1.30e+00	1.72e+00	4.00e+00	4.70e+00	4.09e+00	3.01e-02	5.01e-02	3.21e-02
chr6	34651354	34653894	ENSMUSG00000085382	44.43	-	Gm13861	lncRNA	8.44e-09	2.46e-06	-5.95420	5.36e+00	6.39e+00	5.93e+00	8.02e-02	1.66e-01	1.20e-01	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr3	98222156	98236748	ENSMUSG00000027876	45.23	+	Reg4	protein_coding	8.74e-09	2.50e-06	5.07919	4.58e+00	0.00e+00	3.46e+00	4.61e-02	0.00e+00	2.00e-02	8.62e+00	7.86e+00	9.21e+00	7.88e-01	4.63e-01	1.18e+00
chr16	38396117	38433145	ENSMUSG00000002847	45.28	-	Pla1a	protein_coding	9.98e-09	2.81e-06	-5.33250	8.04e+00	8.04e+00	7.31e+00	2.63e-01	2.63e-01	1.58e-01	2.58e+00	2.58e+00	2.32e+00	5.01e-03	5.01e-03	4.01e-03
chr5	146845071	146847726	ENSMUSG00000029641	50.94	+	Rasl11a	protein_coding	1.07e-08	2.97e-06	-5.68650	6.98e+00	6.30e+00	6.70e+00	2.51e-01	1.56e-01	2.06e-01	0.00e+00	1.58e+00	1.00e+00	0.00e+00	4.01e-03	2.00e-03
chr15	78926725	78930465	ENSMUSG00000068220	55.15	+	Lgals1	protein_coding	1.23e-08	3.35e-06	-5.16045	1.05e+01	1.04e+01	1.06e+01	9.91e-01	8.84e-01	1.06e+00	5.91e+00	5.00e+00	4.95e+00	3.94e-02	2.07e-02	2.00e-02
chr5	90891241	90893115	ENSMUSG00000029380	44.64	+	Cxcl1	protein_coding	1.25e-08	3.35e-06	-5.22578	7.48e+00	8.51e+00	7.39e+00	3.57e-01	7.27e-01	3.35e-01	2.32e+00	3.58e+00	1.00e+00	8.02e-03	2.20e-02	2.00e-03
chr11	102799579	102819700	ENSMUSG00000034520	43.98	-	Gjc1	protein_coding	1.28e-08	3.38e-06	-5.35168	7.58e+00	6.91e+00	7.48e+00	1.27e-01	7.95e-02	1.19e-01	2.32e+00	2.32e+00	1.00e+00	2.67e-03	2.67e-03	6.68e-04
chr10	79860475	79874634	ENSMUSG00000035835	53.50	-	Plppr3	protein_coding	1.35e-08	3.50e-06	-5.83289	6.95e+00	5.95e+00	5.39e+00	4.93e-02	2.44e-02	1.64e-02	0.00e+00	0.00e+00	1.00e+00	0.00e+00	0.00e+00	4.01e-04
chr9	7692090	7699585	ENSMUSG00000018623	41.85	+	Mmp7	protein_coding	1.37e-08	3.50e-06	5.29681	3.17e+00	1.00e+00	4.95e+00	1.60e-02	2.00e-03	6.01e-02	9.95e+00	7.77e+00	8.80e+00	1.97e+00	4.37e-01	8.92e-01
chr3	95499256	95509362	ENSMUSG00000028111	44.59	+	Ctsk	protein_coding	1.45e-08	3.64e-06	-5.83289	6.34e+00	4.39e+00	6.11e+00	1.60e-01	4.01e-02	1.36e-01	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr18	35647267	35649367	ENSMUSG00000024353	52.93	-	Mzb1	protein_coding	1.53e-08	3.72e-06	5.62644	1.58e+00	0.00e+00	0.00e+00	4.01e-03	0.00e+00	0.00e+00	6.66e+00	5.36e+00	6.71e+00	2.00e-01	8.02e-02	2.08e-01
chr11	87566653	87590539	ENSMUSG00000020486	51.03	+	Sept4	protein_coding	1.53e-08	3.72e-06	-5.12338	9.16e+00	9.12e+00	8.38e+00	1.91e-01	1.86e-01	1.11e-01	4.25e+00	2.32e+00	4.17e+00	6.01e-03	1.34e-03	5.68e-03
chr6	4504814	4541544	ENSMUSG00000029661	40.94	+	Col1a2	protein_coding	1.79e-08	4.31e-06	-5.30448	1.10e+01	1.08e+01	1.04e+01	5.05e-01	4.56e-01	3.35e-01	5.43e+00	5.49e+00	5.43e+00	1.05e-02	1.10e-02	1.05e-02
chr11	94936224	94953042	ENSMUSG00000001506	53.92	+	Col1a1	protein_coding	1.85e-08	4.38e-06	-5.85818	1.21e+01	1.23e+01	1.22e+01	2.27e+00	2.60e+00	2.28e+00	6.13e+00	6.81e+00	6.00e+00	3.46e-02	5.56e-02	3.16e-02
chr7	100176502	100184869	ENSMUSG00000035165	47.62	+	Kcne3	protein_coding	2.01e-08	4.68e-06	5.47978	1.00e+00	1.58e+00	1.58e+00	1.00e-03	2.00e-03	2.00e-03	6.77e+00	6.94e+00	6.97e+00	1.08e-01	1.22e-01	1.24e-01
chr3	96601149	96626171	ENSMUSG00000049288	44.78	+	Lix1l	protein_coding	2.09e-08	4.81e-06	-5.08166	7.83e+00	8.00e+00	8.03e+00	2.26e-01	2.56e-01	2.62e-01	1.58e+00	4.00e+00	1.58e+00	2.00e-03	1.50e-02	2.00e-03
chr14	26167405	26182480	ENSMUSG00000072674	48.30	-	Plac9b	protein_coding	2.47e-08	5.56e-06	-4.86086	9.16e+00	9.57e+00	9.39e+00	1.15e+00	1.52e+00	1.34e+00	4.81e+00	4.70e+00	3.91e+00	5.41e-02	5.01e-02	2.81e-02
chr9	48591877	48605153	ENSMUSG00000032271	45.01	-	Nnmt	protein_coding	2.49e-08	5.56e-06	-5.56682	6.41e+00	6.39e+00	6.09e+00	8.42e-02	8.32e-02	6.71e-02	1.58e+00	0.00e+00	0.00e+00	2.00e-03	0.00e+00	0.00e+00
chr14	25887506	25903100	ENSMUSG00000095304	48.14	-	Plac9a	protein_coding	2.53e-08	5.57e-06	-4.97982	7.79e+00	8.38e+00	8.21e+00	4.41e-01	6.67e-01	5.93e-01	3.00e+00	3.81e+00	2.32e+00	1.40e-02	2.61e-02	8.02e-03
chr15	73723145	73758766	ENSMUSG00000059895	55.70	+	Ptp4a3	protein_coding	2.64e-08	5.75e-06	-4.95345	9.58e+00	9.29e+00	9.05e+00	5.10e-01	4.17e-01	3.54e-01	4.86e+00	3.70e+00	4.32e+00	1.87e-02	8.02e-03	1.27e-02
chr6	124512405	124523443	ENSMUSG00000055172	48.07	+	C1ra	protein_coding	3.00e-08	6.45e-06	-5.04920	1.10e+01	9.35e+00	9.32e+00	4.05e+00	1.30e+00	1.28e+00	5.25e+00	5.13e+00	4.75e+00	7.41e-02	6.81e-02	5.21e-02
chr8	21325887	21327020	ENSMUSG00000074437	43.21	-	Defa29	protein_coding	3.08e-08	6.54e-06	5.84130	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	5.55e+00	2.81e+00	6.88e+00	9.22e-02	1.20e-02	2.34e-01
chr7	4772373	4782859	ENSMUSG00000004371	53.62	-	Il11	protein_coding	3.12e-08	6.54e-06	-5.30175	7.22e+00	7.22e+00	5.83e+00	2.97e-01	2.97e-01	1.12e-01	0.00e+00	2.32e+00	1.58e+00	0.00e+00	8.02e-03	4.01e-03
chr10	77052178	77166548	ENSMUSG00000001435	50.81	-	Col18a1	protein_coding	3.26e-08	6.74e-06	-4.92045	1.04e+01	1.02e+01	9.78e+00	9.16e-01	7.82e-01	5.86e-01	5.39e+00	5.32e+00	4.95e+00	2.74e-02	2.61e-02	2.00e-02
chr3	90692632	90695721	ENSMUSG00000056071	42.52	-	S100a9	protein_coding	3.40e-08	6.81e-06	5.65105	1.00e+00	0.00e+00	0.00e+00	2.00e-03	0.00e+00	0.00e+00	7.27e+00	3.17e+00	5.25e+00	3.07e-01	1.60e-02	7.41e-02
chr7	4925785	4944826	ENSMUSG00000035279	50.72	+	Ssc5d	protein_coding	3.41e-08	6.81e-06	-5.61471	7.02e+00	5.09e+00	5.00e+00	1.29e-01	3.31e-02	3.11e-02	1.00e+00	0.00e+00	0.00e+00	1.00e-03	0.00e+00	0.00e+00
chr14	30879200	30881610	ENSMUSG00000042485	54.42	+	Mustn1	protein_coding	3.42e-08	6.81e-06	-5.00856	6.94e+00	8.30e+00	7.89e+00	2.44e-01	6.31e-01	4.73e-01	3.17e+00	2.32e+00	2.81e+00	1.60e-02	8.02e-03	1.20e-02
chr13	16011851	16031621	ENSMUSG00000041324	43.11	+	Inhba	protein_coding	3.62e-08	7.12e-06	-4.95420	5.98e+00	9.24e+00	7.85e+00	1.24e-01	1.21e+00	4.59e-01	3.00e+00	2.81e+00	3.81e+00	1.40e-02	1.20e-02	2.61e-02
chr4	138831857	138835186	ENSMUSG00000058908	52.85	+	Pla2g2a	polymorphic_pseudogene	4.13e-08	8.03e-06	5.03427	3.46e+00	2.00e+00	4.58e+00	1.00e-02	3.01e-03	2.30e-02	9.56e+00	7.74e+00	8.28e+00	7.54e-01	2.13e-01	3.11e-01
chr10	87858265	87937042	ENSMUSG00000020053	41.03	+	Igf1	protein_coding	4.26e-08	8.17e-06	-5.04647	7.76e+00	8.54e+00	6.70e+00	2.16e-01	3.73e-01	1.03e-01	2.58e+00	2.32e+00	3.32e+00	5.01e-03	4.01e-03	9.02e-03
chr2	152931898	152933208	ENSMUSG00000074676	59.19	-	Foxs1	protein_coding	4.45e-08	8.45e-06	-4.97628	8.48e+00	7.50e+00	7.55e+00	7.11e-01	3.61e-01	3.73e-01	3.70e+00	2.58e+00	2.00e+00	2.40e-02	1.00e-02	6.01e-03
chr10	126893113	126893908	ENSMUSG00000112423	52.01	+	Gm6333	processed_pseudogene	5.26e-08	9.79e-06	-4.96000	2.00e+00	0.00e+00	7.93e+00	6.01e-03	0.00e+00	4.87e-01	0.00e+00	2.58e+00	0.00e+00	0.00e+00	1.00e-02	0.00e+00
chr11	5861947	5872088	ENSMUSG00000020473	56.28	+	Aebp1	protein_coding	5.28e-08	9.79e-06	-5.20945	6.88e+00	6.52e+00	6.43e+00	1.17e-01	9.12e-02	8.52e-02	0.00e+00	2.00e+00	1.58e+00	0.00e+00	3.01e-03	2.00e-03

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

DEG table for the contrast APCTOM_vs_APCunsort

The following table presents the top 5% statistically significant genes (use the download links below the table to retrieve the whole list) for the contrast APCTOM_vs_APCunsort. 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_APCTOM_vs_APCunsort	log2_normalized_counts_VK3R22TAPC1	log2_normalized_counts_VK3R23TAPC2	log2_normalized_counts_VK3R24TAPC3	rpgm_normalized_counts_VK3R22TAPC1	rpgm_normalized_counts_VK3R23TAPC2	rpgm_normalized_counts_VK3R24TAPC3	log2_normalized_counts_VK3R16UAPC1	log2_normalized_counts_VK3R17UAPC2	log2_normalized_counts_VK3R18UAPC3	rpgm_normalized_counts_VK3R16UAPC1	rpgm_normalized_counts_VK3R17UAPC2	rpgm_normalized_counts_VK3R18UAPC3
chr7	79391929	79450264	ENSMUSG00000039187	45.00	+	Fanci	protein_coding	1.43e-19	2.33e-15	-13.02536	1.19e+01	1.48e+01	1.06e+01	2.98e-01	2.16e+00	1.16e-01	1.00e+00	0.00e+00	0.00e+00	7.71e-05	0.00e+00	0.00e+00
chr13	63508328	63573598	ENSMUSG00000021466	47.35	-	Ptch1	protein_coding	6.82e-19	4.04e-15	-9.62083	9.24e+00	1.03e+01	8.85e+00	8.07e-02	1.73e-01	6.16e-02	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr14	73183673	73325822	ENSMUSG00000022105	39.21	-	Rb1	protein_coding	7.43e-19	4.04e-15	-9.95728	8.60e+00	4.17e+00	1.29e+01	1.29e-01	5.68e-03	2.52e+00	2.32e+00	0.00e+00	1.00e+00	1.34e-03	0.00e+00	3.34e-04
chr17	47505051	47599691	ENSMUSG00000034165	48.05	+	Ccnd3	protein_coding	1.61e-18	6.58e-15	-10.21371	6.34e+00	1.32e+01	0.00e+00	2.67e-02	3.15e+00	0.00e+00	1.00e+00	1.58e+00	1.58e+00	3.34e-04	6.68e-04	6.68e-04
chr7	130162451	133123350	ENSMUSG00000030849	45.77	-	Fgfr2	protein_coding	2.63e-18	8.60e-15	-10.41926	1.29e+01	7.87e+00	8.46e+00	8.99e-01	2.75e-02	4.15e-02	1.58e+00	0.00e+00	1.00e+00	2.36e-04	0.00e+00	1.18e-04
chr11	87376645	87404954	ENSMUSG00000007646	41.94	-	Rad51c	protein_coding	5.74e-18	1.56e-14	-9.05166	3.81e+00	3.91e+00	1.06e+01	3.72e-03	4.01e-03	4.47e-01	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr9	110532597	110618633	ENSMUSG00000044791	40.36	+	Setd2	protein_coding	5.93e-17	1.38e-13	-8.97988	1.00e+01	1.08e+01	1.14e+01	1.03e-01	1.76e-01	2.77e-01	0.00e+00	2.58e+00	2.00e+00	0.00e+00	5.01e-04	3.01e-04
chr9	95942045	95959393	ENSMUSG00000097777	38.65	-	Gm16794	lncRNA	3.08e-16	6.28e-13	-8.64506	8.71e+00	9.27e+00	7.35e+00	2.10e-01	3.09e-01	8.12e-02	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr8	25513654	25575718	ENSMUSG00000031565	49.44	+	Fgfr1	protein_coding	7.34e-16	1.33e-12	-8.84471	1.08e+01	1.58e+00	1.00e+00	2.62e-01	2.86e-04	1.43e-04	0.00e+00	0.00e+00	1.00e+00	0.00e+00	0.00e+00	1.43e-04
chr9	111228228	111271791	ENSMUSG00000032498	44.21	-	Mlh1	protein_coding	1.20e-15	1.96e-12	-9.59962	1.03e+01	7.44e+00	1.41e+01	1.29e-01	1.82e-02	1.90e+00	2.58e+00	3.17e+00	3.32e+00	5.27e-04	8.44e-04	9.49e-04
chr9	36708482	36727065	ENSMUSG00000032113	39.51	-	Chek1	protein_coding	2.22e-15	3.29e-12	-8.08481	6.07e+00	8.18e+00	9.51e+00	2.65e-02	1.16e-01	2.92e-01	0.00e+00	0.00e+00	1.00e+00	0.00e+00	0.00e+00	4.01e-04
chr10	4611593	5005614	ENSMUSG00000019768	41.08	+	Esr1	protein_coding	8.45e-15	1.15e-11	-8.51439	1.01e+01	0.00e+00	0.00e+00	3.13e-01	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr5	75574916	75656722	ENSMUSG00000005672	46.01	+	Kit	protein_coding	9.12e-14	1.15e-10	-8.05889	9.42e+00	5.55e+00	6.11e+00	2.28e-01	1.54e-02	2.27e-02	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr10	128697939	128705031	ENSMUSG00000025358	47.65	-	Cdk2	protein_coding	1.50e-13	1.74e-10	-7.45327	0.00e+00	8.50e+00	8.40e+00	0.00e+00	1.81e-01	1.68e-01	1.00e+00	0.00e+00	0.00e+00	5.01e-04	0.00e+00	0.00e+00
chr9	53439149	53536740	ENSMUSG00000034218	38.96	-	Atm	protein_coding	7.21e-13	7.85e-10	-7.11201	7.25e+00	8.01e+00	8.71e+00	6.05e-02	1.03e-01	1.68e-01	1.00e+00	0.00e+00	1.58e+00	4.01e-04	0.00e+00	8.02e-04
chr5	143909964	143933968	ENSMUSG00000079109	43.75	+	Pms2	protein_coding	1.80e-12	1.81e-09	-7.16556	1.14e+01	8.65e+00	7.75e+00	6.72e-01	1.00e-01	5.39e-02	1.58e+00	3.32e+00	3.32e+00	5.01e-04	2.25e-03	2.25e-03
chr11	95414080	95493406	ENSMUSG00000057522	40.69	+	Spop	protein_coding	1.88e-12	1.81e-09	-7.20107	9.59e+00	1.58e+00	7.99e+00	5.16e-01	1.34e-03	1.69e-01	1.00e+00	1.00e+00	1.58e+00	6.68e-04	6.68e-04	1.34e-03
chr1	132959484	133030561	ENSMUSG00000054387	43.82	-	Mdm4	protein_coding	2.37e-12	2.14e-09	-7.15381	8.29e+00	7.92e+00	7.28e+00	6.27e-02	4.85e-02	3.09e-02	1.00e+00	0.00e+00	1.00e+00	2.00e-04	0.00e+00	2.00e-04
chr8	21593375	21594508	ENSMUSG00000079113	42.50	-	Gm7861	protein_coding	6.47e-12	5.56e-09	6.85922	0.00e+00	2.00e+00	2.32e+00	0.00e+00	6.01e-03	8.02e-03	8.21e+00	5.17e+00	9.70e+00	5.91e-01	7.01e-02	1.66e+00
chr4	133679008	133756769	ENSMUSG00000007880	46.37	-	Arid1a	protein_coding	7.35e-12	6.00e-09	-6.70201	1.00e+01	1.01e+01	1.21e+01	5.19e-01	5.32e-01	2.18e+00	3.70e+00	4.17e+00	4.95e+00	6.01e-03	8.52e-03	1.50e-02
chr6	145216699	145250239	ENSMUSG00000030265	38.01	-	Kras	protein_coding	1.18e-11	9.17e-09	-7.16603	9.61e+00	1.37e+01	8.21e+00	3.92e-01	6.51e+00	1.48e-01	4.52e+00	5.39e+00	5.04e+00	1.10e-02	2.05e-02	1.60e-02
chr14	31251450	31259944	ENSMUSG00000021901	50.79	+	Bap1	protein_coding	1.45e-11	1.08e-08	-6.83920	8.58e+00	5.55e+00	8.01e+00	8.51e-02	1.02e-02	5.70e-02	1.58e+00	0.00e+00	1.00e+00	4.45e-04	0.00e+00	2.23e-04
chr7	25757273	25788705	ENSMUSG00000002602	50.35	-	Axl	protein_coding	2.10e-11	1.49e-08	-7.33390	8.91e+00	0.00e+00	0.00e+00	1.38e-01	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr6	17463800	17573980	ENSMUSG00000009376	41.79	+	Met	protein_coding	2.55e-11	1.74e-08	-6.93074	9.25e+00	6.92e+00	0.00e+00	2.44e-01	4.81e-02	0.00e+00	1.00e+00	1.00e+00	1.00e+00	4.01e-04	4.01e-04	4.01e-04
chr15	61985391	61990374	ENSMUSG00000022346	52.97	+	Myc	protein_coding	3.49e-11	2.28e-08	-6.86005	8.61e+00	7.96e+00	5.83e+00	3.91e-01	2.48e-01	5.61e-02	0.00e+00	1.00e+00	1.58e+00	0.00e+00	1.00e-03	2.00e-03
chr10	117688875	117710758	ENSMUSG00000020184	42.58	-	Mdm2	protein_coding	4.04e-11	2.53e-08	-6.49452	6.70e+00	5.04e+00	8.66e+00	2.95e-02	9.16e-03	1.15e-01	0.00e+00	2.00e+00	0.00e+00	0.00e+00	8.59e-04	0.00e+00
chr13	58806569	59133970	ENSMUSG00000055254	45.19	+	Ntrk2	protein_coding	4.63e-11	2.80e-08	-6.80367	9.46e+00	2.00e+00	6.17e+00	1.77e-01	7.52e-04	1.78e-02	1.00e+00	1.58e+00	1.00e+00	2.51e-04	5.01e-04	2.51e-04
chr1	65158616	65186500	ENSMUSG00000025950	42.52	-	Idh1	protein_coding	5.08e-11	2.96e-08	-6.05889	3.00e+00	2.32e+00	9.62e+00	2.81e-03	1.60e-03	3.15e-01	1.00e+00	2.81e+00	1.58e+00	4.01e-04	2.40e-03	8.02e-04
chrX	57736547	57738555	ENSMUSG00000080710	40.32	-	Gm8107	processed_pseudogene	1.09e-10	6.12e-08	-7.01123	8.59e+00	0.00e+00	0.00e+00	7.70e-01	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr11	53649519	53707319	ENSMUSG00000020380	42.68	-	Rad50	protein_coding	1.36e-10	7.31e-08	-5.98621	7.77e+00	9.74e+00	8.58e+00	3.97e-02	1.56e-01	6.96e-02	2.00e+00	2.00e+00	3.91e+00	5.47e-04	5.47e-04	2.55e-03

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

DEG table for the contrast APCGFP_vs_APCTOM

The following table presents the top 5% statistically significant genes (use the download links below the table to retrieve the whole list) for the contrast APCGFP_vs_APCTOM. 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_APCGFP_vs_APCTOM	log2_normalized_counts_VK3R19GAPC1	log2_normalized_counts_VK3R20GAPC2	log2_normalized_counts_VK3R21GAPC3	rpgm_normalized_counts_VK3R19GAPC1	rpgm_normalized_counts_VK3R20GAPC2	rpgm_normalized_counts_VK3R21GAPC3	log2_normalized_counts_VK3R22TAPC1	log2_normalized_counts_VK3R23TAPC2	log2_normalized_counts_VK3R24TAPC3	rpgm_normalized_counts_VK3R22TAPC1	rpgm_normalized_counts_VK3R23TAPC2	rpgm_normalized_counts_VK3R24TAPC3
chr7	79391929	79450264	ENSMUSG00000039187	45.00	+	Fanci	protein_coding	5.92e-20	9.66e-16	13.440393	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	1.19e+01	1.48e+01	1.06e+01	2.98e-01	2.16e+00	1.16e-01
chr9	111228228	111271791	ENSMUSG00000032498	44.21	-	Mlh1	protein_coding	2.34e-18	1.91e-14	11.073547	2.81e+00	0.00e+00	0.00e+00	6.33e-04	0.00e+00	0.00e+00	1.03e+01	7.44e+00	1.41e+01	1.29e-01	1.82e-02	1.90e+00
chr11	87376645	87404954	ENSMUSG00000007646	41.94	-	Rad51c	protein_coding	1.42e-17	7.73e-14	9.051662	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	3.81e+00	3.91e+00	1.06e+01	3.72e-03	4.01e-03	4.47e-01
chr14	73183673	73325822	ENSMUSG00000022105	39.21	-	Rb1	protein_coding	7.84e-17	3.20e-13	9.372321	1.58e+00	1.00e+00	2.81e+00	6.68e-04	3.34e-04	2.00e-03	8.60e+00	4.17e+00	1.29e+01	1.29e-01	5.68e-03	2.52e+00
chr17	47505051	47599691	ENSMUSG00000034165	48.05	+	Ccnd3	protein_coding	1.74e-16	5.67e-13	9.126249	2.58e+00	3.17e+00	1.00e+00	1.67e-03	2.67e-03	3.34e-04	6.34e+00	1.32e+01	0.00e+00	2.67e-02	3.15e+00	0.00e+00
chr13	113093825	113100981	ENSMUSG00000023132	43.89	-	Gzma	protein_coding	2.43e-15	5.99e-12	8.108524	1.58e+00	0.00e+00	0.00e+00	4.01e-03	0.00e+00	0.00e+00	7.24e+00	8.91e+00	9.55e+00	3.01e-01	9.60e-01	1.50e+00
chr11	4765845	4849536	ENSMUSG00000009073	44.73	-	Nf2	protein_coding	2.57e-15	5.99e-12	8.557464	0.00e+00	1.00e+00	0.00e+00	0.00e+00	1.25e-04	0.00e+00	1.01e+01	8.04e+00	7.43e+00	1.34e-01	3.29e-02	2.14e-02
chr9	53439149	53536740	ENSMUSG00000034218	38.96	-	Atm	protein_coding	1.19e-14	2.19e-11	8.112005	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	7.25e+00	8.01e+00	8.71e+00	6.05e-02	1.03e-01	1.68e-01
chr7	130162451	133123350	ENSMUSG00000030849	45.77	-	Fgfr2	protein_coding	1.21e-14	2.19e-11	8.756293	2.00e+00	3.58e+00	1.58e+00	3.54e-04	1.30e-03	2.36e-04	1.29e+01	7.87e+00	8.46e+00	8.99e-01	2.75e-02	4.15e-02
chr10	4611593	5005614	ENSMUSG00000019768	41.08	+	Esr1	protein_coding	1.70e-14	2.78e-11	8.514385	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	1.01e+01	0.00e+00	0.00e+00	3.13e-01	0.00e+00	0.00e+00
chr5	143909964	143933968	ENSMUSG00000079109	43.75	+	Pms2	protein_coding	2.13e-14	2.85e-11	8.104162	2.00e+00	2.58e+00	1.00e+00	7.52e-04	1.25e-03	2.51e-04	1.14e+01	8.65e+00	7.75e+00	6.72e-01	1.00e-01	5.39e-02
chr11	87662722	87735539	ENSMUSG00000034177	46.21	+	Rnf43	protein_coding	2.18e-14	2.85e-11	8.395177	0.00e+00	0.00e+00	2.00e+00	0.00e+00	0.00e+00	1.20e-03	1.08e+01	3.81e+00	7.69e+00	7.21e-01	5.21e-03	8.26e-02
chr9	110532597	110618633	ENSMUSG00000044791	40.36	+	Setd2	protein_coding	2.27e-14	2.85e-11	7.854349	3.17e+00	2.58e+00	3.17e+00	8.02e-04	5.01e-04	8.02e-04	1.00e+01	1.08e+01	1.14e+01	1.03e-01	1.76e-01	2.77e-01
chr10	128697939	128705031	ENSMUSG00000025358	47.65	-	Cdk2	protein_coding	3.87e-14	4.52e-11	7.868308	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	8.50e+00	8.40e+00	0.00e+00	1.81e-01	1.68e-01
chr9	36708482	36727065	ENSMUSG00000032113	39.51	-	Chek1	protein_coding	7.88e-14	8.57e-11	7.499846	0.00e+00	2.00e+00	0.00e+00	0.00e+00	1.20e-03	0.00e+00	6.07e+00	8.18e+00	9.51e+00	2.65e-02	1.16e-01	2.92e-01
chr6	145216699	145250239	ENSMUSG00000030265	38.01	-	Kras	protein_coding	1.04e-13	1.06e-10	8.458817	3.17e+00	2.81e+00	4.58e+00	4.01e-03	3.01e-03	1.15e-02	9.61e+00	1.37e+01	8.21e+00	3.92e-01	6.51e+00	1.48e-01
chr1	132959484	133030561	ENSMUSG00000054387	43.82	-	Mdm4	protein_coding	1.29e-13	1.24e-10	7.890771	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	8.29e+00	7.92e+00	7.28e+00	6.27e-02	4.85e-02	3.09e-02
chr13	63508328	63573598	ENSMUSG00000021466	47.35	-	Ptch1	protein_coding	1.38e-13	1.25e-10	7.505354	1.58e+00	2.00e+00	2.58e+00	2.67e-04	4.01e-04	6.68e-04	9.24e+00	1.03e+01	8.85e+00	8.07e-02	1.73e-01	6.16e-02
chr8	25513654	25575718	ENSMUSG00000031565	49.44	+	Fgfr1	protein_coding	1.86e-13	1.60e-10	7.844706	2.00e+00	0.00e+00	1.58e+00	4.29e-04	0.00e+00	2.86e-04	1.08e+01	1.58e+00	1.00e+00	2.62e-01	2.86e-04	1.43e-04
chr9	95942045	95959393	ENSMUSG00000097777	38.65	-	Gm16794	lncRNA	2.58e-13	2.10e-10	7.645058	1.00e+00	0.00e+00	1.58e+00	5.01e-04	0.00e+00	1.00e-03	8.71e+00	9.27e+00	7.35e+00	2.10e-01	3.09e-01	8.12e-02
chr11	82876434	82890614	ENSMUSG00000018841	48.53	-	Rad51d	protein_coding	2.82e-13	2.19e-10	7.915879	1.00e+00	0.00e+00	0.00e+00	2.86e-04	0.00e+00	0.00e+00	9.75e+00	6.32e+00	4.70e+00	2.46e-01	2.26e-02	7.16e-03
chr6	17463800	17573980	ENSMUSG00000009376	41.79	+	Met	protein_coding	4.00e-13	2.97e-10	7.930737	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	9.25e+00	6.92e+00	0.00e+00	2.44e-01	4.81e-02	0.00e+00
chr11	95414080	95493406	ENSMUSG00000057522	40.69	+	Spop	protein_coding	3.67e-12	2.60e-09	7.423466	0.00e+00	0.00e+00	2.00e+00	0.00e+00	0.00e+00	2.00e-03	9.59e+00	1.58e+00	7.99e+00	5.16e-01	1.34e-03	1.69e-01
chr17	31141049	31144282	ENSMUSG00000024028	50.15	-	Tff2	protein_coding	7.87e-12	5.35e-09	6.528780	0.00e+00	1.00e+00	1.58e+00	0.00e+00	2.00e-03	4.01e-03	4.75e+00	5.88e+00	8.87e+00	5.21e-02	1.16e-01	9.36e-01
chr11	53649519	53707319	ENSMUSG00000020380	42.68	-	Rad50	protein_coding	1.04e-11	6.80e-09	6.422312	3.46e+00	0.00e+00	2.32e+00	1.82e-03	0.00e+00	7.29e-04	7.77e+00	9.74e+00	8.58e+00	3.97e-02	1.56e-01	6.96e-02
chr11	121036747	121039418	ENSMUSG00000025163	52.13	-	Cd7	protein_coding	1.98e-11	1.24e-08	6.816984	1.00e+00	0.00e+00	0.00e+00	1.00e-03	0.00e+00	0.00e+00	5.21e+00	7.44e+00	7.91e+00	3.61e-02	1.73e-01	2.39e-01
chr13	58806569	59133970	ENSMUSG00000055254	45.19	+	Ntrk2	protein_coding	3.06e-11	1.85e-08	7.026062	2.00e+00	0.00e+00	0.00e+00	7.52e-04	0.00e+00	0.00e+00	9.46e+00	2.00e+00	6.17e+00	1.77e-01	7.52e-04	1.78e-02
chr7	122107262	122132985	ENSMUSG00000044702	44.00	-	Palb2	protein_coding	3.94e-11	2.30e-08	7.179909	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	8.34e+00	6.25e+00	5.17e+00	4.96e-02	1.16e-02	5.40e-03
chr1	65158616	65186500	ENSMUSG00000025950	42.52	-	Idh1	protein_coding	5.55e-11	3.12e-08	6.321928	1.58e+00	2.00e+00	1.58e+00	8.02e-04	1.20e-03	8.02e-04	3.00e+00	2.32e+00	9.62e+00	2.81e-03	1.60e-03	3.15e-01
chr14	56017971	56020384	ENSMUSG00000022227	47.31	+	Mcpt1	protein_coding	6.16e-11	3.35e-08	6.058172	0.00e+00	4.70e+00	1.58e+00	0.00e+00	5.01e-02	4.01e-03	7.98e+00	9.14e+00	1.02e+01	5.05e-01	1.13e+00	2.36e+00
chr19	58728887	58744169	ENSMUSG00000042179	45.92	+	Pnliprp1	protein_coding	8.33e-11	4.39e-08	6.382470	0.00e+00	1.58e+00	1.58e+00	0.00e+00	4.45e-04	4.45e-04	7.36e+00	8.25e+00	6.86e+00	3.63e-02	6.75e-02	2.56e-02
chr5	75574916	75656722	ENSMUSG00000005672	46.01	+	Kit	protein_coding	1.37e-10	7.01e-08	6.643856	0.00e+00	2.58e+00	0.00e+00	0.00e+00	1.67e-03	0.00e+00	9.42e+00	5.55e+00	6.11e+00	2.28e-01	1.54e-02	2.27e-02
chr2	28641228	28691167	ENSMUSG00000026812	46.34	+	Tsc1	protein_coding	1.68e-10	8.32e-08	6.152647	2.00e+00	1.58e+00	4.39e+00	3.76e-04	2.51e-04	2.51e-03	9.24e+00	9.07e+00	9.73e+00	7.57e-02	6.70e-02	1.06e-01
chr4	133679008	133756769	ENSMUSG00000007880	46.37	-	Arid1a	protein_coding	2.09e-10	1.00e-07	5.969701	5.39e+00	5.58e+00	3.70e+00	2.05e-02	2.35e-02	6.01e-03	1.00e+01	1.01e+01	1.21e+01	5.19e-01	5.32e-01	2.18e+00
chrX	105797615	105929403	ENSMUSG00000031229	38.38	-	Atrx	protein_coding	2.20e-10	1.03e-07	6.651704	4.25e+00	5.64e+00	4.25e+00	1.29e-03	3.51e-03	1.29e-03	1.27e+01	9.41e+00	1.05e+01	4.81e-01	4.86e-02	1.04e-01
chr13	21286429	21292731	ENSMUSG00000004344	44.49	-	Gpx5	protein_coding	2.31e-10	1.05e-07	6.619609	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	6.77e+00	6.70e+00	6.36e+00	2.16e-01	2.06e-01	1.62e-01
chr4	117096075	117116101	ENSMUSG00000028681	52.47	+	Ptch2	protein_coding	2.87e-10	1.26e-07	6.930737	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	8.28e+00	5.78e+00	0.00e+00	3.87e-02	6.76e-03	0.00e+00
chr2	119112793	119147445	ENSMUSG00000027323	43.41	+	Rad51	protein_coding	3.16e-10	1.36e-07	6.842350	1.00e+00	0.00e+00	0.00e+00	3.34e-04	0.00e+00	0.00e+00	8.70e+00	5.39e+00	0.00e+00	1.39e-01	1.37e-02	0.00e+00
chr3	98013527	98150361	ENSMUSG00000027878	46.82	+	Notch2	protein_coding	3.93e-10	1.65e-07	6.554589	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	4.70e+00	7.99e+00	0.00e+00	2.51e-02	2.55e-01	0.00e+00
chr5	150522630	150570329	ENSMUSG00000041147	41.71	+	Brca2	protein_coding	1.02e-09	4.18e-07	6.239996	0.00e+00	3.32e+00	0.00e+00	0.00e+00	1.64e-03	0.00e+00	9.79e+00	1.00e+00	4.09e+00	1.62e-01	1.82e-04	2.91e-03
chr4	138831857	138835186	ENSMUSG00000058908	52.85	+	Pla2g2a	polymorphic_pseudogene	1.93e-09	7.66e-07	5.625241	3.46e+00	2.00e+00	4.58e+00	1.00e-02	3.01e-03	2.30e-02	9.27e+00	9.22e+00	9.47e+00	6.16e-01	5.98e-01	7.11e-01
chr10	52045721	52195244	ENSMUSG00000019893	40.47	-	Ros1	protein_coding	2.42e-09	9.39e-07	6.353147	0.00e+00	1.00e+00	0.00e+00	0.00e+00	6.68e-05	0.00e+00	7.57e+00	7.07e+00	1.58e+00	1.26e-02	8.88e-03	1.34e-04
chr16	3979105	4003770	ENSMUSG00000039738	48.54	-	Slx4	protein_coding	3.07e-09	1.17e-06	6.076816	0.00e+00	0.00e+00	1.00e+00	0.00e+00	0.00e+00	1.34e-04	4.86e+00	3.17e+00	7.86e+00	3.74e-03	1.07e-03	3.09e-02
chr3	20215620	20242181	ENSMUSG00000001865	37.40	-	Cpa3	protein_coding	3.71e-09	1.38e-06	6.142958	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	5.43e+00	5.67e+00	6.88e+00	4.21e-02	5.01e-02	1.17e-01
chr6	87383256	87388935	ENSMUSG00000030048	48.79	-	Gkn3	protein_coding	4.97e-09	1.80e-06	5.418501	5.49e+00	1.00e+00	4.17e+00	8.82e-02	2.00e-03	3.41e-02	9.95e+00	9.34e+00	1.02e+01	1.98e+00	1.29e+00	2.29e+00
chr4	134082448	134095082	ENSMUSG00000000682	49.53	-	Cd52	protein_coding	6.28e-09	2.23e-06	5.693487	0.00e+00	0.00e+00	1.00e+00	0.00e+00	0.00e+00	6.68e-04	5.49e+00	5.39e+00	6.91e+00	2.94e-02	2.74e-02	7.95e-02
chr19	58759719	58777534	ENSMUSG00000025091	46.53	+	Pnliprp2	protein_coding	7.80e-09	2.62e-06	5.546894	2.00e+00	0.00e+00	1.58e+00	6.01e-03	0.00e+00	4.01e-03	6.41e+00	7.30e+00	7.03e+00	1.68e-01	3.15e-01	2.61e-01
chr6	79048116	79048824	ENSMUSG00000107772	49.93	+	Gm5311	processed_pseudogene	7.85e-09	2.62e-06	-5.837943	0.00e+00	1.00e+00	8.14e+00	0.00e+00	2.00e-03	5.65e-01	1.00e+00	0.00e+00	1.00e+00	2.00e-03	0.00e+00	2.00e-03
chr1	169655501	169695813	ENSMUSG00000026678	41.35	+	Rgs5	protein_coding	7.85e-09	2.62e-06	-5.241690	9.35e+00	9.27e+00	1.00e+01	6.51e-01	6.17e-01	1.04e+00	4.09e+00	4.39e+00	4.52e+00	1.60e-02	2.00e-02	2.20e-02
chr11	78499091	78502324	ENSMUSG00000017344	53.06	+	Vtn	protein_coding	8.67e-09	2.83e-06	-6.031219	6.34e+00	6.63e+00	7.20e+00	5.34e-02	6.55e-02	9.75e-02	0.00e+00	0.00e+00	1.58e+00	0.00e+00	0.00e+00	1.34e-03
chr14	56042041	56044642	ENSMUSG00000022226	46.93	+	Mcpt2	protein_coding	9.07e-09	2.90e-06	5.102487	2.00e+00	5.09e+00	4.39e+00	6.01e-03	6.61e-02	4.01e-02	7.46e+00	9.13e+00	1.03e+01	3.51e-01	1.12e+00	2.59e+00
chr14	31251450	31259944	ENSMUSG00000021901	50.79	+	Bap1	protein_coding	9.91e-09	3.11e-06	5.616811	1.00e+00	2.81e+00	2.32e+00	2.23e-04	1.34e-03	8.91e-04	8.58e+00	5.55e+00	8.01e+00	8.51e-02	1.02e-02	5.70e-02
chr3	103058285	103067914	ENSMUSG00000027852	42.00	+	Nras	protein_coding	1.16e-08	3.58e-06	5.428843	1.58e+00	2.32e+00	2.32e+00	5.73e-04	1.15e-03	1.15e-03	6.11e+00	8.74e+00	5.95e+00	1.95e-02	1.23e-01	1.75e-02
chr6	87345650	87350942	ENSMUSG00000030050	43.13	-	Gkn1	protein_coding	1.26e-08	3.82e-06	5.267772	1.58e+00	2.81e+00	3.17e+00	4.01e-03	1.20e-02	1.60e-02	7.56e+00	8.23e+00	7.92e+00	3.77e-01	6.01e-01	4.83e-01
chr7	144929931	144939925	ENSMUSG00000070348	53.37	-	Ccnd1	protein_coding	1.43e-08	4.25e-06	5.224373	1.58e+00	2.32e+00	4.17e+00	1.34e-03	2.67e-03	1.14e-02	3.81e+00	9.88e+00	4.00e+00	8.68e-03	6.29e-01	1.00e-02
chr1	171518122	171535294	ENSMUSG00000038209	42.50	-	Itln1	protein_coding	2.38e-08	6.87e-06	4.884864	3.32e+00	2.81e+00	2.32e+00	9.02e-03	6.01e-03	4.01e-03	7.32e+00	7.94e+00	7.93e+00	1.59e-01	2.45e-01	2.43e-01
chr10	117688875	117710758	ENSMUSG00000020184	42.58	-	Mdm2	protein_coding	2.40e-08	6.87e-06	5.272130	0.00e+00	3.17e+00	2.00e+00	0.00e+00	2.29e-03	8.59e-04	6.70e+00	5.04e+00	8.66e+00	2.95e-02	9.16e-03	1.15e-01
chr7	144838083	144844436	ENSMUSG00000031074	56.81	+	Fgf3	protein_coding	2.61e-08	7.35e-06	5.815917	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	2.81e+00	7.27e+00	3.00e+00	6.01e-03	1.53e-01	7.01e-03
chr11	83525778	83530518	ENSMUSG00000035042	46.64	-	Ccl5	protein_coding	2.66e-08	7.35e-06	5.081084	1.58e+00	2.58e+00	3.46e+00	2.00e-03	5.01e-03	1.00e-02	5.61e+00	7.97e+00	8.56e+00	4.81e-02	2.49e-01	3.78e-01
chr7	24777206	24784657	ENSMUSG00000054169	44.99	+	Ceacam10	protein_coding	3.14e-08	8.55e-06	5.572890	0.00e+00	0.00e+00	1.58e+00	0.00e+00	0.00e+00	2.00e-03	6.49e+00	6.07e+00	6.34e+00	8.92e-02	6.61e-02	8.02e-02
chr7	25757273	25788705	ENSMUSG00000002602	50.35	-	Axl	protein_coding	4.28e-08	1.15e-05	5.748938	1.00e+00	2.00e+00	1.58e+00	2.86e-04	8.59e-04	5.73e-04	8.91e+00	0.00e+00	0.00e+00	1.38e-01	0.00e+00	0.00e+00
chr9	21616169	21704230	ENSMUSG00000032187	47.08	+	Smarca4	protein_coding	4.87e-08	1.27e-05	5.271992	5.52e+00	4.91e+00	4.39e+00	4.51e-02	2.91e-02	2.00e-02	1.18e+01	6.48e+00	6.58e+00	3.57e+00	8.82e-02	9.52e-02
chr10	59879556	59899302	ENSMUSG00000020109	52.15	+	Dnajb12	protein_coding	4.97e-08	1.27e-05	-5.602884	0.00e+00	0.00e+00	7.91e+00	0.00e+00	0.00e+00	8.02e-02	0.00e+00	0.00e+00	1.58e+00	0.00e+00	0.00e+00	6.68e-04
chr13	23535422	23535951	ENSMUSG00000099517	61.70	+	Hist1h3g	protein_coding	4.98e-08	1.27e-05	6.073249	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	7.46e+00	0.00e+00	4.64e+00	3.51e-01	0.00e+00	4.81e-02

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

Download the raw read counts table for the experiment.
Download the normalized read counts table for the experiment.

References

Statham, A.L., Strbenac, D., Coolen, M.W., Stirzaker, C., Clark, S.J., Robinson, M.D. (2010). Repitools: an R package for the analysis of enrichment-based epigenomic data. Bioinformatics 26(13), 1662-1663.
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.