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RNAseq - Alignment and differential expression analysis

Title: GE6680
Project: (none)
Started on: 6/12/2023 12:09:01
Hostname: login1.ufhpc
Run directory: /blue/licht/runs/LeleLabCollab/GE6680/GE6680
Configuration GE6680.conf

Table of contents:

Input data
Trimming and quality control
Alignment to transcriptome
Genome coverage
Expression analysis - quantification
Differential expression - protein-coding genes
Differential expression - all genes
Differential expression - isoform level
Differential expression - combined files
Alternative splicing analysis
MultiQC report
UCSC hub
Methods summary

1. Input data
The following table summarizes the samples, conditions, and contrasts in this analysis. A readset is either a single fastq file or a pair of fastq files (for paired-end sequencing).

Category	Data
*Summary of input data*
Experimental conditions:	None0_NuMUG, dNesp_IAA0_NuMUG, dNesp_IAA2_NuMUG, dNesp_IAA8_NuMUG, None24_NuMUG, None_DMSO_NuMUG, IAA24_NuMUG, dNesp_DMSO_NuMUG, dNesp_IAA24_NuMUG
Contrasts:	dNesp_IAA0_NuMUG vs. IAA24_NuMUG, dNesp_IAA2_NuMUG vs. IAA24_NuMUG, dNesp_IAA8_NuMUG vs. IAA24_NuMUG, dNesp_IAA24_NuMUG vs. IAA24_NuMUG, dNesp_DMSO_NuMUG vs. dNesp_IAA0_NuMUG, None0_NuMUG vs. dNesp_DMSO_NuMUG, None0_NuMUG vs. dNesp_IAA0_NuMUG, dNesp_IAA24_NuMUG vs. dNesp_DMSO_NuMUG, dNesp_IAA24_NuMUG vs. None0_NuMUG, dNesp_IAA2_NuMUG vs. dNesp_IAA0_NuMUG, dNesp_IAA8_NuMUG vs. dNesp_IAA0_NuMUG, dNesp_IAA24_NuMUG vs. dNesp_IAA0_NuMUG
Number of samples	27
*Sequencing data data*
Total number of reads:	945,167,613
Average reads per sample:	35,006,207

Table 1. Summary of input data

Condition	Sample	Number of reads	% Reads
None0_NuMUG	None0_NuMUG_1a	30,635,375	3.24%
	None0_NuMUG_1b	33,835,035	3.58%
	None0_NuMUG_1c	38,594,389	4.08%
dNesp_IAA0_NuMUG	dNesp_IAA0_NuMUG_2a	43,320,496	4.58%
	dNesp_IAA0_NuMUG_2b	30,802,954	3.26%
	dNesp_IAA0_NuMUG_2c	36,897,259	3.90%
dNesp_IAA2_NuMUG	dNesp_IAA2_NuMUG_3a	45,420,822	4.81%
	dNesp_IAA2_NuMUG_3b	35,075,164	3.71%
	dNesp_IAA2_NuMUG_3c	31,211,011	3.30%
dNesp_IAA8_NuMUG	dNesp_IAA8_NuMUG_4a	41,923,323	4.44%
	dNesp_IAA8_NuMUG_4b	37,992,451	4.02%
	dNesp_IAA8_NuMUG_4c	31,500,163	3.33%
None24_NuMUG	None24_NuMUG_5a	31,525,877	3.34%
	None24_NuMUG_5b	37,287,498	3.95%
	None24_NuMUG_5c	36,894,870	3.90%
None_DMSO_NuMUG	None_DMSO_NuMUG_6a	32,270,758	3.41%
	None_DMSO_NuMUG_6b	26,699,048	2.82%
	None_DMSO_NuMUG_6c	35,816,163	3.79%
IAA24_NuMUG	IAA24_NuMUG_7a	46,021,674	4.87%
	IAA24_NuMUG_7b	29,285,538	3.10%
	IAA24_NuMUG_7c	31,814,955	3.37%
dNesp_DMSO_NuMUG	dNesp_DMSO_NuMUG_8a	37,184,646	3.93%
	dNesp_DMSO_NuMUG_8b	32,746,285	3.46%
	dNesp_DMSO_NuMUG_8c	34,877,859	3.69%
dNesp_IAA24_NuMUG	dNesp_IAA24_NuMUG_9a	32,529,490	3.44%
	dNesp_IAA24_NuMUG_9b	28,107,093	2.97%
	dNesp_IAA24_NuMUG_9c	34,897,417	3.69%

Table 2. Number of reads in each sample.

2. Trimming and quality control
The input sequences were trimmed using trimmomatic. Quality control was performed before and after trimming using FastQC. The following table provides links to the quality control reports before and after trimming, as well as the number of reads in the trimmed files.

Sample	Readset	Reads before trim	QC before trim	Reads after trim	QC after trim	% Retained
None0_NuMUG_1a	None0_NuMUG_1a_r1	30,635,375	1a_S1_L002_R1_001 1a_S1_L002_R2_001	29,327,161	1a_S1_L002_R1_001.trim.paired 1a_S1_L002_R2_001.trim.paired	95.73%
None0_NuMUG_1b	None0_NuMUG_1b_r1	33,835,035	1b_S2_L002_R1_001 1b_S2_L002_R2_001	32,452,765	1b_S2_L002_R1_001.trim.paired 1b_S2_L002_R2_001.trim.paired	95.91%
None0_NuMUG_1c	None0_NuMUG_1c_r1	38,594,389	1c_S3_L002_R1_001 1c_S3_L002_R2_001	37,089,750	1c_S3_L002_R1_001.trim.paired 1c_S3_L002_R2_001.trim.paired	96.10%
dNesp_IAA0_NuMUG_2a	dNesp_IAA0_NuMUG_2a_r1	43,320,496	2a_S4_L002_R1_001 2a_S4_L002_R2_001	41,246,306	2a_S4_L002_R1_001.trim.paired 2a_S4_L002_R2_001.trim.paired	95.21%
dNesp_IAA0_NuMUG_2b	dNesp_IAA0_NuMUG_2b_r1	30,802,954	2b_S5_L002_R1_001 2b_S5_L002_R2_001	29,543,853	2b_S5_L002_R1_001.trim.paired 2b_S5_L002_R2_001.trim.paired	95.91%
dNesp_IAA0_NuMUG_2c	dNesp_IAA0_NuMUG_2c_r1	36,897,259	2c_S6_L002_R1_001 2c_S6_L002_R2_001	35,504,492	2c_S6_L002_R1_001.trim.paired 2c_S6_L002_R2_001.trim.paired	96.23%
dNesp_IAA2_NuMUG_3a	dNesp_IAA2_NuMUG_3a_r1	45,420,822	3a_S7_L002_R1_001 3a_S7_L002_R2_001	43,656,657	3a_S7_L002_R1_001.trim.paired 3a_S7_L002_R2_001.trim.paired	96.12%
dNesp_IAA2_NuMUG_3b	dNesp_IAA2_NuMUG_3b_r1	35,075,164	3b_S8_L002_R1_001 3b_S8_L002_R2_001	33,656,492	3b_S8_L002_R1_001.trim.paired 3b_S8_L002_R2_001.trim.paired	95.96%
dNesp_IAA2_NuMUG_3c	dNesp_IAA2_NuMUG_3c_r1	31,211,011	3c_S9_L002_R1_001 3c_S9_L002_R2_001	29,974,294	3c_S9_L002_R1_001.trim.paired 3c_S9_L002_R2_001.trim.paired	96.04%
dNesp_IAA8_NuMUG_4a	dNesp_IAA8_NuMUG_4a_r1	41,923,323	4a_S10_L002_R1_001 4a_S10_L002_R2_001	40,287,969	4a_S10_L002_R1_001.trim.paired 4a_S10_L002_R2_001.trim.paired	96.10%
dNesp_IAA8_NuMUG_4b	dNesp_IAA8_NuMUG_4b_r1	37,992,451	4b_S11_L002_R1_001 4b_S11_L002_R2_001	36,527,884	4b_S11_L002_R1_001.trim.paired 4b_S11_L002_R2_001.trim.paired	96.15%
dNesp_IAA8_NuMUG_4c	dNesp_IAA8_NuMUG_4c_r1	31,500,163	4c_S12_L002_R1_001 4c_S12_L002_R2_001	30,088,110	4c_S12_L002_R1_001.trim.paired 4c_S12_L002_R2_001.trim.paired	95.52%
None24_NuMUG_5a	None24_NuMUG_5a_r1	31,525,877	5a_S13_L002_R1_001 5a_S13_L002_R2_001	30,041,296	5a_S13_L002_R1_001.trim.paired 5a_S13_L002_R2_001.trim.paired	95.29%
None24_NuMUG_5b	None24_NuMUG_5b_r1	37,287,498	5b_S14_L002_R1_001 5b_S14_L002_R2_001	35,841,916	5b_S14_L002_R1_001.trim.paired 5b_S14_L002_R2_001.trim.paired	96.12%
None24_NuMUG_5c	None24_NuMUG_5c_r1	36,894,870	5c_S15_L002_R1_001 5c_S15_L002_R2_001	35,394,429	5c_S15_L002_R1_001.trim.paired 5c_S15_L002_R2_001.trim.paired	95.93%
None_DMSO_NuMUG_6a	None_DMSO_NuMUG_6a_r1	32,270,758	6a_S16_L002_R1_001 6a_S16_L002_R2_001	30,952,188	6a_S16_L002_R1_001.trim.paired 6a_S16_L002_R2_001.trim.paired	95.91%
None_DMSO_NuMUG_6b	None_DMSO_NuMUG_6b_r1	26,699,048	6b_S17_L002_R1_001 6b_S17_L002_R2_001	25,682,623	6b_S17_L002_R1_001.trim.paired 6b_S17_L002_R2_001.trim.paired	96.19%
None_DMSO_NuMUG_6c	None_DMSO_NuMUG_6c_r1	35,816,163	6c_S18_L002_R1_001 6c_S18_L002_R2_001	34,403,846	6c_S18_L002_R1_001.trim.paired 6c_S18_L002_R2_001.trim.paired	96.06%
IAA24_NuMUG_7a	IAA24_NuMUG_7a_r1	46,021,674	7a_S19_L002_R1_001 7a_S19_L002_R2_001	44,165,726	7a_S19_L002_R1_001.trim.paired 7a_S19_L002_R2_001.trim.paired	95.97%
IAA24_NuMUG_7b	IAA24_NuMUG_7b_r1	29,285,538	7b_S20_L002_R1_001 7b_S20_L002_R2_001	28,155,161	7b_S20_L002_R1_001.trim.paired 7b_S20_L002_R2_001.trim.paired	96.14%
IAA24_NuMUG_7c	IAA24_NuMUG_7c_r1	31,814,955	7c_S21_L002_R1_001 7c_S21_L002_R2_001	30,897,225	7c_S21_L002_R1_001.trim.paired 7c_S21_L002_R2_001.trim.paired	97.12%
dNesp_DMSO_NuMUG_8a	dNesp_DMSO_NuMUG_8a_r1	37,184,646	8a_S22_L002_R1_001 8a_S22_L002_R2_001	35,792,889	8a_S22_L002_R1_001.trim.paired 8a_S22_L002_R2_001.trim.paired	96.26%
dNesp_DMSO_NuMUG_8b	dNesp_DMSO_NuMUG_8b_r1	32,746,285	8b_S23_L002_R1_001 8b_S23_L002_R2_001	31,468,267	8b_S23_L002_R1_001.trim.paired 8b_S23_L002_R2_001.trim.paired	96.10%
dNesp_DMSO_NuMUG_8c	dNesp_DMSO_NuMUG_8c_r1	34,877,859	8c_S24_L002_R1_001 8c_S24_L002_R2_001	33,467,429	8c_S24_L002_R1_001.trim.paired 8c_S24_L002_R2_001.trim.paired	95.96%
dNesp_IAA24_NuMUG_9a	dNesp_IAA24_NuMUG_9a_r1	32,529,490	9a_S25_L002_R1_001 9a_S25_L002_R2_001	31,417,458	9a_S25_L002_R1_001.trim.paired 9a_S25_L002_R2_001.trim.paired	96.58%
dNesp_IAA24_NuMUG_9b	dNesp_IAA24_NuMUG_9b_r1	28,107,093	9b_S26_L002_R1_001 9b_S26_L002_R2_001	26,998,985	9b_S26_L002_R1_001.trim.paired 9b_S26_L002_R2_001.trim.paired	96.06%
dNesp_IAA24_NuMUG_9c	dNesp_IAA24_NuMUG_9c_r1	34,897,417	9c_S27_L002_R1_001 9c_S27_L002_R2_001	33,613,324	9c_S27_L002_R1_001.trim.paired 9c_S27_L002_R2_001.trim.paired	96.32%

Table 3. Number of reads in input files and links to QC reports.

The following two tables report the number of reads before and after QC in each sample and in each condition.

Sample	Reads before QC	Reads after QC	% Retained
None0_NuMUG_1a	30,635,375	29,327,161	95.73%
None0_NuMUG_1b	33,835,035	32,452,765	95.91%
None0_NuMUG_1c	38,594,389	37,089,750	96.10%
dNesp_IAA0_NuMUG_2a	43,320,496	41,246,306	95.21%
dNesp_IAA0_NuMUG_2b	30,802,954	29,543,853	95.91%
dNesp_IAA0_NuMUG_2c	36,897,259	35,504,492	96.23%
dNesp_IAA2_NuMUG_3a	45,420,822	43,656,657	96.12%
dNesp_IAA2_NuMUG_3b	35,075,164	33,656,492	95.96%
dNesp_IAA2_NuMUG_3c	31,211,011	29,974,294	96.04%
dNesp_IAA8_NuMUG_4a	41,923,323	40,287,969	96.10%
dNesp_IAA8_NuMUG_4b	37,992,451	36,527,884	96.15%
dNesp_IAA8_NuMUG_4c	31,500,163	30,088,110	95.52%
None24_NuMUG_5a	31,525,877	30,041,296	95.29%
None24_NuMUG_5b	37,287,498	35,841,916	96.12%
None24_NuMUG_5c	36,894,870	35,394,429	95.93%
None_DMSO_NuMUG_6a	32,270,758	30,952,188	95.91%
None_DMSO_NuMUG_6b	26,699,048	25,682,623	96.19%
None_DMSO_NuMUG_6c	35,816,163	34,403,846	96.06%
IAA24_NuMUG_7a	46,021,674	44,165,726	95.97%
IAA24_NuMUG_7b	29,285,538	28,155,161	96.14%
IAA24_NuMUG_7c	31,814,955	30,897,225	97.12%
dNesp_DMSO_NuMUG_8a	37,184,646	35,792,889	96.26%
dNesp_DMSO_NuMUG_8b	32,746,285	31,468,267	96.10%
dNesp_DMSO_NuMUG_8c	34,877,859	33,467,429	95.96%
dNesp_IAA24_NuMUG_9a	32,529,490	31,417,458	96.58%
dNesp_IAA24_NuMUG_9b	28,107,093	26,998,985	96.06%
dNesp_IAA24_NuMUG_9c	34,897,417	33,613,324	96.32%

Table 4. Number of reads in each sample before and after QC.

Condition	Reads before QC	Reads after QC	% Retained
None0_NuMUG	103,064,799	98,869,676	95.93%
dNesp_IAA0_NuMUG	111,020,709	106,294,651	95.74%
dNesp_IAA2_NuMUG	111,706,997	107,287,443	96.04%
dNesp_IAA8_NuMUG	111,415,937	106,903,963	95.95%
None24_NuMUG	105,708,245	101,277,641	95.81%
None_DMSO_NuMUG	94,785,969	91,038,657	96.05%
IAA24_NuMUG	107,122,167	103,218,112	96.36%
dNesp_DMSO_NuMUG	104,808,790	100,728,585	96.11%
dNesp_IAA24_NuMUG	95,534,000	92,029,767	96.33%

Table 5. Number of reads in each condition before and after QC.

3. Alignment to transcriptome
The input sequences were aligned to the transcriptome using 2.7.9a. The following table reports the number of alignments to the genome and the transcriptome for each sample. Please note that the number of alignments will in general be higher than the number of reads because the same read may align to multiple isoforms of the same gene. The WIG files can be uploaded to the UCSC Genome Browser as custom tracks.

Sample	Input reads	Genome alignments	Genome alignment rate	Transcriptome alignments	Transcriptome alignment rate	Alignment report
None0_NuMUG_1a	29,327,161	57,858,771	1.97	24,298,330	82.85%	None0_NuMUG_1a.star/Log.final.out
None0_NuMUG_1b	32,452,765	64,215,825	1.98	27,226,088	83.89%	None0_NuMUG_1b.star/Log.final.out
None0_NuMUG_1c	37,089,750	73,597,337	1.98	31,194,093	84.10%	None0_NuMUG_1c.star/Log.final.out
dNesp_IAA0_NuMUG_2a	41,246,306	82,779,845	2.01	35,206,224	85.36%	dNesp_IAA0_NuMUG_2a.star/Log.final.out
dNesp_IAA0_NuMUG_2b	29,543,853	59,224,484	2.00	24,848,883	84.11%	dNesp_IAA0_NuMUG_2b.star/Log.final.out
dNesp_IAA0_NuMUG_2c	35,504,492	70,998,809	2.00	30,373,414	85.55%	dNesp_IAA0_NuMUG_2c.star/Log.final.out
dNesp_IAA2_NuMUG_3a	43,656,657	87,214,436	2.00	37,021,413	84.80%	dNesp_IAA2_NuMUG_3a.star/Log.final.out
dNesp_IAA2_NuMUG_3b	33,656,492	66,625,065	1.98	28,274,555	84.01%	dNesp_IAA2_NuMUG_3b.star/Log.final.out
dNesp_IAA2_NuMUG_3c	29,974,294	59,546,577	1.99	25,321,899	84.48%	dNesp_IAA2_NuMUG_3c.star/Log.final.out
dNesp_IAA8_NuMUG_4a	40,287,969	80,123,424	1.99	33,700,444	83.65%	dNesp_IAA8_NuMUG_4a.star/Log.final.out
dNesp_IAA8_NuMUG_4b	36,527,884	73,341,371	2.01	31,347,190	85.82%	dNesp_IAA8_NuMUG_4b.star/Log.final.out
dNesp_IAA8_NuMUG_4c	30,088,110	60,116,807	2.00	25,696,022	85.40%	dNesp_IAA8_NuMUG_4c.star/Log.final.out
None24_NuMUG_5a	30,041,296	60,580,345	2.02	25,647,846	85.38%	None24_NuMUG_5a.star/Log.final.out
None24_NuMUG_5b	35,841,916	72,262,175	2.02	30,780,232	85.88%	None24_NuMUG_5b.star/Log.final.out
None24_NuMUG_5c	35,394,429	71,025,067	2.01	30,307,772	85.63%	None24_NuMUG_5c.star/Log.final.out
None_DMSO_NuMUG_6a	30,952,188	60,998,541	1.97	25,526,849	82.47%	None_DMSO_NuMUG_6a.star/Log.final.out
None_DMSO_NuMUG_6b	25,682,623	51,424,515	2.00	21,571,999	83.99%	None_DMSO_NuMUG_6b.star/Log.final.out
None_DMSO_NuMUG_6c	34,403,846	68,799,744	2.00	29,203,257	84.88%	None_DMSO_NuMUG_6c.star/Log.final.out
IAA24_NuMUG_7a	44,165,726	89,451,314	2.03	37,523,937	84.96%	IAA24_NuMUG_7a.star/Log.final.out
IAA24_NuMUG_7b	28,155,161	55,472,060	1.97	23,855,838	84.73%	IAA24_NuMUG_7b.star/Log.final.out
IAA24_NuMUG_7c	30,897,225	62,718,179	2.03	26,703,638	86.43%	IAA24_NuMUG_7c.star/Log.final.out
dNesp_DMSO_NuMUG_8a	35,792,889	69,224,876	1.93	29,406,043	82.16%	dNesp_DMSO_NuMUG_8a.star/Log.final.out
dNesp_DMSO_NuMUG_8b	31,468,267	62,671,260	1.99	26,295,873	83.56%	dNesp_DMSO_NuMUG_8b.star/Log.final.out
dNesp_DMSO_NuMUG_8c	33,467,429	66,254,147	1.98	27,912,709	83.40%	dNesp_DMSO_NuMUG_8c.star/Log.final.out
dNesp_IAA24_NuMUG_9a	31,417,458	62,995,517	2.01	26,055,339	82.93%	dNesp_IAA24_NuMUG_9a.star/Log.final.out
dNesp_IAA24_NuMUG_9b	26,998,985	53,877,440	2.00	22,944,793	84.98%	dNesp_IAA24_NuMUG_9b.star/Log.final.out
dNesp_IAA24_NuMUG_9c	33,613,324	67,372,590	2.00	28,629,098	85.17%	dNesp_IAA24_NuMUG_9c.star/Log.final.out

Table 6. Number of alignments to genome and transcriptome.

4. Genome coverage
The following table reports the overall and effective genome coverage in each sample. The Total nt column reports the total number of nucleotides sequenced, i.e. the number of aligned reads times the length of each read. Coverage is this number divided by the size of the genome. Effective bp reports the number of bases in the genome having coverage greater than 5, and the Effective Perc column shows what percentage this is of the genome size. Note that, especially in the case of RNA-seq, the effective genome size may be much smaller than the full size. Eff Coverage is the average coverage over the effectively covered fraction of the genome.

Name	Total nt	Coverage	Effective bp	Effective Perc	Eff Coverage
None0_NuMUG_1a	51,141,521,793	18.80	501,698,883	18.40%	101.94
None0_NuMUG_1b	56,364,946,038	20.71	514,651,746	18.90%	109.52
None0_NuMUG_1c	65,601,052,237	24.10	525,567,573	19.30%	124.82
dNesp_IAA0_NuMUG_2a	75,215,270,785	27.63	535,363,604	19.70%	140.49
dNesp_IAA0_NuMUG_2b	55,065,876,123	20.23	502,405,046	18.50%	109.60
dNesp_IAA0_NuMUG_2c	64,996,759,349	23.88	519,487,601	19.10%	125.12
dNesp_IAA2_NuMUG_3a	80,519,534,190	29.57	537,228,232	19.70%	149.88
dNesp_IAA2_NuMUG_3b	59,202,825,304	21.75	511,680,825	18.80%	115.70
dNesp_IAA2_NuMUG_3c	54,617,100,616	20.07	500,686,620	18.40%	109.08
dNesp_IAA8_NuMUG_4a	74,807,020,436	27.49	531,770,141	19.50%	140.68
dNesp_IAA8_NuMUG_4b	63,339,360,654	23.27	521,615,719	19.20%	121.43
dNesp_IAA8_NuMUG_4c	52,683,417,742	19.36	509,989,377	18.70%	103.30
None24_NuMUG_5a	54,036,042,509	19.85	529,438,590	19.40%	102.06
None24_NuMUG_5b	60,864,998,099	22.35	548,330,462	20.10%	111.00
None24_NuMUG_5c	61,256,953,908	22.52	538,606,309	19.80%	113.73
None_DMSO_NuMUG_6a	54,016,549,503	19.84	523,977,113	19.20%	103.09
None_DMSO_NuMUG_6b	46,227,797,412	16.98	508,433,274	18.70%	90.92
None_DMSO_NuMUG_6c	62,983,818,404	23.13	537,921,134	19.80%	117.09
IAA24_NuMUG_7a	83,713,271,275	30.75	564,279,053	20.70%	148.35
IAA24_NuMUG_7b	47,202,719,661	17.34	522,622,307	19.20%	90.32
IAA24_NuMUG_7c	55,103,292,598	20.24	531,573,843	19.50%	103.66
dNesp_DMSO_NuMUG_8a	60,875,893,451	22.36	531,952,487	19.50%	114.44
dNesp_DMSO_NuMUG_8b	56,343,512,776	20.69	522,892,242	19.20%	107.75
dNesp_DMSO_NuMUG_8c	56,887,764,097	20.89	528,038,374	19.40%	107.73
dNesp_IAA24_NuMUG_9a	60,253,613,391	22.14	520,101,264	19.10%	115.85
dNesp_IAA24_NuMUG_9b	50,244,595,118	18.45	516,426,251	19.00%	97.29
dNesp_IAA24_NuMUG_9c	60,673,708,991	22.28	536,554,398	19.70%	113.08

Table 7. Genome coverage by sample.

The following table reports the overall and effective genome coverage in each condition.

Name	Total nt	Coverage	Effective bp	Effective Perc	Eff Coverage
None0_NuMUG	169,605,001,625	62.28	601,169,325	22.10%	282.13
dNesp_IAA0_NuMUG	190,900,590,563	70.09	609,410,408	22.40%	313.25
dNesp_IAA2_NuMUG	189,944,414,065	69.74	605,818,586	22.20%	313.53
dNesp_IAA8_NuMUG	186,934,202,270	68.63	613,495,517	22.50%	304.70
None24_NuMUG	173,639,347,678	63.75	643,026,998	23.60%	270.03
None_DMSO_NuMUG	161,171,246,935	59.18	617,988,496	22.70%	260.80
IAA24_NuMUG	183,189,545,158	67.25	641,674,456	23.60%	285.49
dNesp_DMSO_NuMUG	171,405,730,143	62.91	626,616,773	23.00%	273.54
dNesp_IAA24_NuMUG	168,565,664,803	61.87	624,236,472	22.90%	270.03

Table 8. Genome coverage by condition

File: GE6680.sample.cov.xlsx
Size: 85.19 kB
Description: Per-chromosome coverage data, by sample.

File: GE6680.cond.cov.xlsx
Size: 35.00 kB
Description: Per-chromosome coverage data, by condition.

5. Expression analysis - quantification
Gene and transcript expression values were quantified using RSEM v1.3.1. The following files contain the raw FPKM values for all genes/transcripts in all samples. NOTE: these values are not normalized yet, please apply the appropriate normalization before using them in analysis.

File: genes.rawmatrix.csv
Size: 6.78 MB
Description: Matrix of FPKM values for all genes in all samples.

File: transcripts.rawmatrix.csv
Size: 17.07 MB
Description: Matrix of FPKM values for all transcripts in all samples.

File: genes.xpra.txt
Size: 6.13 MB
Description: Counts table suitable for ExpressAnalyst.

The following scatterplots show the level of similarity between replicates of the same condition.

Principal Component Analysis on raw (un-normalized) expression data. Click on the thumbnail to display the full-size image.

(png format, 370.12 kB)

The following image displays the Multi-Dimensional Scaling (MDS) plot for the raw (un-normalized) expression data. Click on the thumbnail to display the full-size image.

6. Differential expression - protein-coding genes
Differential gene expression was analyzed using DESeq2. The following table reports the number of differentially expressed genes in each contrast with abs(log2(FC)) >= 0.58 and FDR-corrected P-value <= 0.05. The files under the Table heading contain the log2(FC) and P-value of all significant genes, while the files under the Expressions heading contain normalized expression values for the significant genes in all replicates of the two conditions being compared. The lists of differentially expressed genes for all contrasts can also be downloaded as a single Excel file using the link below.

Test	Control	Total	Overexpressed	Underexpressed	Table	Expressions
dNesp_IAA0_NuMUG	IAA24_NuMUG	3,880	1,801	2,079	dNesp_IAA0_NuMUG.vs.IAA24_NuMUG.codinggeneDiff.csv	dNesp_IAA0_NuMUG.vs.IAA24_NuMUG.gmatrix.csv
dNesp_IAA2_NuMUG	IAA24_NuMUG	3,745	1,695	2,050	dNesp_IAA2_NuMUG.vs.IAA24_NuMUG.codinggeneDiff.csv	dNesp_IAA2_NuMUG.vs.IAA24_NuMUG.gmatrix.csv
dNesp_IAA8_NuMUG	IAA24_NuMUG	3,432	1,532	1,900	dNesp_IAA8_NuMUG.vs.IAA24_NuMUG.codinggeneDiff.csv	dNesp_IAA8_NuMUG.vs.IAA24_NuMUG.gmatrix.csv
dNesp_IAA24_NuMUG	IAA24_NuMUG	814	394	420	dNesp_IAA24_NuMUG.vs.IAA24_NuMUG.codinggeneDiff.csv	dNesp_IAA24_NuMUG.vs.IAA24_NuMUG.gmatrix.csv
dNesp_DMSO_NuMUG	dNesp_IAA0_NuMUG	2,414	1,383	1,031	dNesp_DMSO_NuMUG.vs.dNesp_IAA0_NuMUG.codinggeneDiff.csv	dNesp_DMSO_NuMUG.vs.dNesp_IAA0_NuMUG.gmatrix.csv
None0_NuMUG	dNesp_DMSO_NuMUG	2,266	957	1,309	None0_NuMUG.vs.dNesp_DMSO_NuMUG.codinggeneDiff.csv	None0_NuMUG.vs.dNesp_DMSO_NuMUG.gmatrix.csv
None0_NuMUG	dNesp_IAA0_NuMUG	626	358	268	None0_NuMUG.vs.dNesp_IAA0_NuMUG.codinggeneDiff.csv	None0_NuMUG.vs.dNesp_IAA0_NuMUG.gmatrix.csv
dNesp_IAA24_NuMUG	dNesp_DMSO_NuMUG	2	1	1	dNesp_IAA24_NuMUG.vs.dNesp_DMSO_NuMUG.codinggeneDiff.csv	dNesp_IAA24_NuMUG.vs.dNesp_DMSO_NuMUG.gmatrix.csv
dNesp_IAA24_NuMUG	None0_NuMUG	2,388	1,364	1,024	dNesp_IAA24_NuMUG.vs.None0_NuMUG.codinggeneDiff.csv	dNesp_IAA24_NuMUG.vs.None0_NuMUG.gmatrix.csv
dNesp_IAA2_NuMUG	dNesp_IAA0_NuMUG	61	20	41	dNesp_IAA2_NuMUG.vs.dNesp_IAA0_NuMUG.codinggeneDiff.csv	dNesp_IAA2_NuMUG.vs.dNesp_IAA0_NuMUG.gmatrix.csv
dNesp_IAA8_NuMUG	dNesp_IAA0_NuMUG	170	53	117	dNesp_IAA8_NuMUG.vs.dNesp_IAA0_NuMUG.codinggeneDiff.csv	dNesp_IAA8_NuMUG.vs.dNesp_IAA0_NuMUG.gmatrix.csv
dNesp_IAA24_NuMUG	dNesp_IAA0_NuMUG	2,550	1,433	1,117	dNesp_IAA24_NuMUG.vs.dNesp_IAA0_NuMUG.codinggeneDiff.csv	dNesp_IAA24_NuMUG.vs.dNesp_IAA0_NuMUG.gmatrix.csv

Table 9. Results of gene-level differential expression analysis.

File: GE6680-codingdiff.xlsx
Size: 1.56 MB
Description: Excel file containing differentially expressed genes for all contrasts (one sheet per contrast). Only includes protein-coding genes.

File: GE6680-allcodingdiff.xlsx
Size: 9.74 MB
Description: Excel file containing differential expression values for all tested genes in all contrasts (one sheet per contrast). Only includes protein-coding genes. Note: genes with very low average expression in all conditions were removed.

File: GE6680.g.deseq2norm.xlsx
Size: 5.12 MB
Description: Excel file containing normalized (DESeq2) expression values for all protein-coding genes in all conditions. Note: genes with very low average expression in all conditions were removed.

Principal Component Analysis on normalized expression data. Click on the thumbnail to display the full-size image.

(png format, 372.09 kB)

The following image displays the Multi-Dimensional Scaling (MDS) plot for the normalized expression data. In this plot, relative distances between samples reflect the similarity of their gene expression profiles. Ideally, replicates of the same condition should be close together, and well separated from other conditions.

Volcano plots for all contrasts. Use the menu to select a contrast.

7. Differential expression - all genes
The following table reports results from the same differential analysis as above, but includes all biotypes instead of coding genes only.

Test	Control	Total	Overexpressed	Underexpressed	Table	Expressions
dNesp_IAA0_NuMUG	IAA24_NuMUG	4,219	1,948	2,271	dNesp_IAA0_NuMUG.vs.IAA24_NuMUG.geneDiff.csv	dNesp_IAA0_NuMUG.vs.IAA24_NuMUG.gmatrix.csv
dNesp_IAA2_NuMUG	IAA24_NuMUG	4,114	1,850	2,264	dNesp_IAA2_NuMUG.vs.IAA24_NuMUG.geneDiff.csv	dNesp_IAA2_NuMUG.vs.IAA24_NuMUG.gmatrix.csv
dNesp_IAA8_NuMUG	IAA24_NuMUG	3,798	1,701	2,097	dNesp_IAA8_NuMUG.vs.IAA24_NuMUG.geneDiff.csv	dNesp_IAA8_NuMUG.vs.IAA24_NuMUG.gmatrix.csv
dNesp_IAA24_NuMUG	IAA24_NuMUG	873	417	456	dNesp_IAA24_NuMUG.vs.IAA24_NuMUG.geneDiff.csv	dNesp_IAA24_NuMUG.vs.IAA24_NuMUG.gmatrix.csv
dNesp_DMSO_NuMUG	dNesp_IAA0_NuMUG	2,633	1,520	1,113	dNesp_DMSO_NuMUG.vs.dNesp_IAA0_NuMUG.geneDiff.csv	dNesp_DMSO_NuMUG.vs.dNesp_IAA0_NuMUG.gmatrix.csv
None0_NuMUG	dNesp_DMSO_NuMUG	2,500	1,040	1,460	None0_NuMUG.vs.dNesp_DMSO_NuMUG.geneDiff.csv	None0_NuMUG.vs.dNesp_DMSO_NuMUG.gmatrix.csv
None0_NuMUG	dNesp_IAA0_NuMUG	651	366	285	None0_NuMUG.vs.dNesp_IAA0_NuMUG.geneDiff.csv	None0_NuMUG.vs.dNesp_IAA0_NuMUG.gmatrix.csv
dNesp_IAA24_NuMUG	dNesp_DMSO_NuMUG	3	1	2	dNesp_IAA24_NuMUG.vs.dNesp_DMSO_NuMUG.geneDiff.csv	dNesp_IAA24_NuMUG.vs.dNesp_DMSO_NuMUG.gmatrix.csv
dNesp_IAA24_NuMUG	None0_NuMUG	2,639	1,509	1,130	dNesp_IAA24_NuMUG.vs.None0_NuMUG.geneDiff.csv	dNesp_IAA24_NuMUG.vs.None0_NuMUG.gmatrix.csv
dNesp_IAA2_NuMUG	dNesp_IAA0_NuMUG	73	25	48	dNesp_IAA2_NuMUG.vs.dNesp_IAA0_NuMUG.geneDiff.csv	dNesp_IAA2_NuMUG.vs.dNesp_IAA0_NuMUG.gmatrix.csv
dNesp_IAA8_NuMUG	dNesp_IAA0_NuMUG	207	72	135	dNesp_IAA8_NuMUG.vs.dNesp_IAA0_NuMUG.geneDiff.csv	dNesp_IAA8_NuMUG.vs.dNesp_IAA0_NuMUG.gmatrix.csv
dNesp_IAA24_NuMUG	dNesp_IAA0_NuMUG	2,794	1,556	1,238	dNesp_IAA24_NuMUG.vs.dNesp_IAA0_NuMUG.geneDiff.csv	dNesp_IAA24_NuMUG.vs.dNesp_IAA0_NuMUG.gmatrix.csv

Table 10. Results of gene-level differential expression analysis (all biotypes).

File: GE6680-genediff.xlsx
Size: 1.72 MB
Description: Excel file containing differentially expressed genes for all contrasts (one sheet per contrast). Includes all genes and pseudo-genes.

File: GE6680-allgenediff.xlsx
Size: 11.13 MB
Description: Excel file containing differential expression values for all genes in all contrasts (one sheet per contrast). Includes all genes and pseudo-genes.

File: GE6680-allExpressions.xlsx
Size: 4.39 MB
Description: Excel file containing normalized (RSEM) expression values for all genes in all conditions.

8. Differential expression - isoform level
The following table reports the number of differentially expressed isoforms in each contrast with abs(log2(FC)) >= 0.58 and FDR-corrected P-value <= 0.05. The lists of differentially expressed isoforms for all contrasts can also be downloaded as a single Excel file using the link below.

Test	Control	Tot isoforms	Overexpressed	Underexpressed	Table	Expressions
dNesp_IAA0_NuMUG	IAA24_NuMUG	0	0	0	dNesp_IAA0_NuMUG.vs.IAA24_NuMUG.isoDiff.csv	dNesp_IAA0_NuMUG.vs.IAA24_NuMUG.imatrix.csv
dNesp_IAA2_NuMUG	IAA24_NuMUG	0	0	0	dNesp_IAA2_NuMUG.vs.IAA24_NuMUG.isoDiff.csv	dNesp_IAA2_NuMUG.vs.IAA24_NuMUG.imatrix.csv
dNesp_IAA8_NuMUG	IAA24_NuMUG	0	0	0	dNesp_IAA8_NuMUG.vs.IAA24_NuMUG.isoDiff.csv	dNesp_IAA8_NuMUG.vs.IAA24_NuMUG.imatrix.csv
dNesp_IAA24_NuMUG	IAA24_NuMUG	0	0	0	dNesp_IAA24_NuMUG.vs.IAA24_NuMUG.isoDiff.csv	dNesp_IAA24_NuMUG.vs.IAA24_NuMUG.imatrix.csv
dNesp_DMSO_NuMUG	dNesp_IAA0_NuMUG	0	0	0	dNesp_DMSO_NuMUG.vs.dNesp_IAA0_NuMUG.isoDiff.csv	dNesp_DMSO_NuMUG.vs.dNesp_IAA0_NuMUG.imatrix.csv
None0_NuMUG	dNesp_DMSO_NuMUG	0	0	0	None0_NuMUG.vs.dNesp_DMSO_NuMUG.isoDiff.csv	None0_NuMUG.vs.dNesp_DMSO_NuMUG.imatrix.csv
None0_NuMUG	dNesp_IAA0_NuMUG	0	0	0	None0_NuMUG.vs.dNesp_IAA0_NuMUG.isoDiff.csv	None0_NuMUG.vs.dNesp_IAA0_NuMUG.imatrix.csv
dNesp_IAA24_NuMUG	dNesp_DMSO_NuMUG	0	0	0	dNesp_IAA24_NuMUG.vs.dNesp_DMSO_NuMUG.isoDiff.csv	dNesp_IAA24_NuMUG.vs.dNesp_DMSO_NuMUG.imatrix.csv
dNesp_IAA24_NuMUG	None0_NuMUG	0	0	0	dNesp_IAA24_NuMUG.vs.None0_NuMUG.isoDiff.csv	dNesp_IAA24_NuMUG.vs.None0_NuMUG.imatrix.csv
dNesp_IAA2_NuMUG	dNesp_IAA0_NuMUG	0	0	0	dNesp_IAA2_NuMUG.vs.dNesp_IAA0_NuMUG.isoDiff.csv	dNesp_IAA2_NuMUG.vs.dNesp_IAA0_NuMUG.imatrix.csv
dNesp_IAA8_NuMUG	dNesp_IAA0_NuMUG	0	0	0	dNesp_IAA8_NuMUG.vs.dNesp_IAA0_NuMUG.isoDiff.csv	dNesp_IAA8_NuMUG.vs.dNesp_IAA0_NuMUG.imatrix.csv
dNesp_IAA24_NuMUG	dNesp_IAA0_NuMUG	0	0	0	dNesp_IAA24_NuMUG.vs.dNesp_IAA0_NuMUG.isoDiff.csv	dNesp_IAA24_NuMUG.vs.dNesp_IAA0_NuMUG.imatrix.csv

Table 11. Results of isoform-level differential expression analysis.

File: GE6680-isodiff.xlsx
Size: 11.28 kB
Description: Excel file containing differentially expressed isoforms for all contrasts (one sheet per contrast).

File: GE6680-allisodiff.xlsx
Size: 11.13 MB
Description: Excel file containing differential expression values for all isoforms in all contrasts (one sheet per contrast).

9. Differential expression - combined files
The following file contains merged differential expression data. The first sheet contains fold changes for all genes that were found to be differentially expressed in at least one contrast. The second and third sheets contain the same information for coding genes only, and all transcripts.

File: GE6680-merged.allDiff.xlsx
Size: 1.46 MB
Description: Merged fold changes for all differentially expressed genes, coding genes, and transcripts respectively.

10. Alternative splicing analysis
Alternative splicing analysis was performed using rMATS version v4.1.0. The following table reports the number of events in each class for each contrast. The link in the last column allows you to download an Excel file containing full results.

Test	Control	SE	RI	MXE	A3SS	A5SS	Full
dNesp_IAA0_NuMUG	IAA24_NuMUG	190	78	33	58	37	dNesp_IAA0_NuMUG.vs.IAA24_NuMUG.MATS.xlsx
dNesp_IAA2_NuMUG	IAA24_NuMUG	194	84	37	45	33	dNesp_IAA2_NuMUG.vs.IAA24_NuMUG.MATS.xlsx
dNesp_IAA8_NuMUG	IAA24_NuMUG	159	89	29	49	31	dNesp_IAA8_NuMUG.vs.IAA24_NuMUG.MATS.xlsx
dNesp_IAA24_NuMUG	IAA24_NuMUG	147	69	29	47	29	dNesp_IAA24_NuMUG.vs.IAA24_NuMUG.MATS.xlsx
dNesp_DMSO_NuMUG	dNesp_IAA0_NuMUG	158	79	35	53	38	dNesp_DMSO_NuMUG.vs.dNesp_IAA0_NuMUG.MATS.xlsx
None0_NuMUG	dNesp_DMSO_NuMUG	176	95	36	44	30	None0_NuMUG.vs.dNesp_DMSO_NuMUG.MATS.xlsx
None0_NuMUG	dNesp_IAA0_NuMUG	156	58	28	52	27	None0_NuMUG.vs.dNesp_IAA0_NuMUG.MATS.xlsx
dNesp_IAA24_NuMUG	dNesp_DMSO_NuMUG	172	52	36	39	31	dNesp_IAA24_NuMUG.vs.dNesp_DMSO_NuMUG.MATS.xlsx
dNesp_IAA24_NuMUG	None0_NuMUG	173	76	39	42	33	dNesp_IAA24_NuMUG.vs.None0_NuMUG.MATS.xlsx
dNesp_IAA2_NuMUG	dNesp_IAA0_NuMUG	147	59	27	53	34	dNesp_IAA2_NuMUG.vs.dNesp_IAA0_NuMUG.MATS.xlsx
dNesp_IAA8_NuMUG	dNesp_IAA0_NuMUG	147	61	32	53	30	dNesp_IAA8_NuMUG.vs.dNesp_IAA0_NuMUG.MATS.xlsx
dNesp_IAA24_NuMUG	dNesp_IAA0_NuMUG	181	75	35	50	34	dNesp_IAA24_NuMUG.vs.dNesp_IAA0_NuMUG.MATS.xlsx

Table 12. Number of alternative splicing events, by class, for each contrast. Classes are: SE=exon skipping; RI=intron retention; MXE=mutually exclusive exons; A3SS=alternative 3' splice site; A5SS=alternative 5' splice site.

11. MultiQC report
MultiQC is a general Quality Control tool for a large number of bioinformatics pipelines. The report on this analysis (generated using MultiQC version 1.12) is available here:

MultiQC report

12. UCSC hub

UCSC Genome Browser: use the previous link to display the data tracks automatically, or copy the the URL https://bw:bw@data.rc.ufl.edu/secure/icbr/LLC//GE6680/hub/hub.txt and paste it into the "My Hubs" form in this page.

WashU EpiGenome Browser: use the previous link to display the data tracks automatically, or copy the following URL into the "Datahub by URL Link" field: https://bw:bw@data.rc.ufl.edu/secure/icbr/LLC//GE6680/hub/hub.json.

13. Methods summary

Short reads were trimmed using trimmomatic (v 0.36) [1], and QC on the original and trimmed reads was performed using FastQC (v 0.11.4) [2] and MultiQC [3].

The reads were aligned to the transcriptome using STAR version 2.7.9a [4].

Transcript abundance was quantified using RSEM (RSEM v1.3.1) [5].

Differential expression analysis was performed using DESeq2 [6], with an FDR-corrected P-value threshold of 0.05. The output files were further filtered to extract transcripts showing a 1.5-fold change in either direction. Results were reported for protein-coding genes only, and for all transcript types.

Alternative splicing analysis was performed using rMATS version v4.1.0 [7].

References

Bolger, A. M., Lohse, M., and Usadel, B. (2014). Trimmomatic: A flexible trimmer for Illumina Sequence Data. Bioinformatics, btu170.
https://www.bioinformatics.babraham.ac.uk/projects/fastqc/
Philip Ewels, Mans Magnusson, Sverker Lundin and Max Kaller (2016). MultiQC: Summarize analysis results for multiple tools and samples in a single report. Bioinformatics | doi: 10.1093/bioinformatics/btw354 | PubMed: 27312411
Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR (2013). STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 29(1):15-21 | doi: 10.1093/bioinformatics/bts635
Li B and Dewey CN (2011). RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12:323 | doi: 10.1186/1471-2105-12-323
Love MI, huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15,550 (2014). | doi: 10.1186/s13059-014-0550-8
Shen S., Park JW., Lu ZX., Lin L., Henry MD., Wu YN., Zhou Q., Xing Y. rMATS: Robust and Flexible Detection of Differential Alternative Splicing from Replicate RNA-Seq Data. PNAS, 111(51):E5593-601 | doi: 10.1073/pnas.1419161111

Completed: 6-12-2023@12:11