KaWaiData.md

RNA-Seq data from Ka-Wai Ma

Table of Contents

• 1. Introduction
• 2. Tasks
  • 2.1 Alignment
  • 2.2 Quantification
• 3. Progress
  • 3.1 Pre-processing
  • 3.2 Alignment
• 4. Results
  • 4.1 Aligned reads
  • 4.2 DEGs
  • 4.3 Cluster
    • 4.3.1 Hierarchical clustering
    • 4.3.2 k-means clustering
  • 4.4 Gene-set analysis
• 5. 1stadd
  • 5.1 Aligned reads
  • 5.2 DEGs
  • 5.3 Cluster
• 6. Comparison
• 7. Soil
  • 7.1 Aligned reads
• References

1. Introduction

The Arabidopsis thaliana Col-0 treated with flg22, SynCom33+flg22, and SynCom35+flg22.

reference genomes path /netscratch/dep_psl/grp_rgo/yniu/ref

RNA-seq data path /biodata/dep_psl/grp_rgo/metatranscriptomics/data/flg22

sample number	genotype At	organ	flg22	bacteria	biological replicate	Library title	Library number	Library state	Reads requested	Reads sequenced
a	pWER::FLS2-GFP	roots	0.00	no	1	a.1	4016.A.2	Draft	20,000,000	0.0
b	pWER::FLS2-GFP	roots	1.00	no	1	b.1	4016.B.2	Draft	20,000,000	0.0
c	pWER::FLS2-GFP	roots	1.00	SynCom33	1	c.1	4016.C.2	Draft	20,000,000	0.0
d	pWER::FLS2-GFP	roots	1.00	SynCom35	1	d.1	4016.D.2	Draft	20,000,000	0.0
e	pWER::FLS2-GFP	roots	0.00	no	2	e.1	4016.E.2	Draft	20,000,000	0.0
f	pWER::FLS2-GFP	roots	1.00	no	2	f.1	4016.F.2	Draft	20,000,000	0.0
g	pWER::FLS2-GFP	roots	1.00	SynCom33	2	g.1	4016.G.2	Draft	20,000,000	0.0
h	pWER::FLS2-GFP	roots	1.00	SynCom35	2	h.1	4016.H.2	Draft	20,000,000	0.0
i	pWER::FLS2-GFP	roots	0.00	no	3	i.1	4016.I.2	Draft	20,000,000	0.0
j	pWER::FLS2-GFP	roots	1.00	no	3	j.1	4016.J.2	Draft	20,000,000	0.0
k	pWER::FLS2-GFP	roots	1.00	SynCom33	3	k.1	4016.K.2	Draft	20,000,000	0.0
l	pWER::FLS2-GFP	roots	1.00	SynCom35	3	l.1	4016.L.2	Draft	20,000,000	0.0

analysis path /netscratch/dep_psl/grp_rgo/yniu/KaWaiFlg22

2. Tasks

2.1 Alignment

• Align reads to Arabidopsis thaliana Col-0 genomes (HISAT2) and cDNAs (Kallisto)

2.2 Quantification

• Quantification transcripts.

• Differentially expressed genes.

3. Progress

3.1 Pre-processing

• Trim by fastp with stringent parameters that remove the 8-mer in the head and 2-mer in the tail of each read.

3.2 Alignment

• Use Kallisto and HISAT2 to align reads.

4. Results

4.1 Aligned reads

• Alignment rates

sample	rawfq	trimfq	H_ath	K_ath
Mock_1	16978228	16881903	0.977	0.962
Mock_2	18851317	18723047	0.979	0.966
Mock_3	18313224	18218407	0.98	0.966
Flg22_1	17512255	17422675	0.971	0.954
Flg22_2	18047704	17950283	0.975	0.959
Flg22_3	17922052	17826125	0.974	0.957
Flg22_SynCom33_1	17785611	17687693	0.974	0.955
Flg22_SynCom33_2	16929833	16832003	0.975	0.96
Flg22_SynCom33_3	20093432	19984770	0.974	0.956
Flg22_SynCom35_1	17361283	17267286	0.965	0.952
Flg22_SynCom35_2	16914038	16825207	0.979	0.966
Flg22_SynCom35_3	21203518	21099083	0.972	0.957

"K" and "H" is for Kallisto and HISAT2 alignment.

• Kallisto alignment example

• HISAT2 alignment example

• PCA plot

rlog (regularized log transformation) in PCA.

4.2 DEGs

                      condition
Mock_1                     Mock
Mock_2                     Mock
Mock_3                     Mock
Flg22_1                   Flg22
Flg22_2                   Flg22
Flg22_3                   Flg22
Flg22_SynCom33_1 Flg22_SynCom33
Flg22_SynCom33_2 Flg22_SynCom33
Flg22_SynCom33_3 Flg22_SynCom33
Flg22_SynCom35_1 Flg22_SynCom35
Flg22_SynCom35_2 Flg22_SynCom35
Flg22_SynCom35_3 Flg22_SynCom35

• 3 groups (flg22, flg22_SynCom33, and flg22_SynCom35) vs. Mock

results/eachGroup_vs_Mock_k_full.csv (remove genes with zero count) and results/eachGroup_vs_Mock_k.csv (remove gene with at least two zero count in one condition).

4.3 Cluster

4.3.1 Hierarchical clustering

Hierarchical clustering to find potential gene expression patterns in four conditions (Mock, flg22, SynCom33, and SynCom35). The read count of each transcript was scaled, then average value was used to represent each condition. Transcript with more than one zero counts in any of the four conditions were excluded (29161 transcripts left).

Distance was calculated as 1 - Pearson's Correlation Coefficient

• Cut trees to get clusters

  height threshold 0.5

  

  AT1G14550.1 AT2G30750.1 AT2G19190.1 
         21          26          20 
  

  height threshold 1.0

  

  AT1G14550.1 AT2G30750.1 AT2G19190.1 
          9          14          14 
  

  height threshold 1.5

  AT1G14550.1 AT2G30750.1 AT2G19190.1 
          7           7           7 
  

  

  

• correlation of traits (phenotype) and clusters

  flg22 SynCom33 SynCom35 bacteria rootlen
      0        0        0        0     5.6
      1        0        0        0     1.1
      1        1        0        1     1.3
      1        0        1        1     4.9
  

  

• heatmaps

4.3.2 k-means clustering

Choose k (group number) by the sum of squared error (SSE) and Akaike information criterion (AIC). k number 10 was finally chose.

AT1G14550.1 AT2G30750.1 AT2G19190.1 AT5G24110.1 
          4           4           2           4 

• correlation of traits (phenotype) and clusters

  flg22 SynCom33 SynCom35 bacteria rootlen
      0        0        0        0     5.6
      1        0        0        0     1.1
      1        1        0        1     1.3
      1        0        1        1     4.9

• heatmaps

4.4 Gene-set analysis

Find significantly changed gene ontology, KEGG, and BioCyc gene-sets in each k-means cluster.

5. 1stadd

5.1 aligned reads

sample	rawfq	trimfq	H_ath	K_ath	Hvirus_ath	Kvirus_ath
HKSynCom33_1	6133228	6009879	0.469	0.461	0.977	0.982
HKSynCom33_2	6509281	6214550	0.654	0.645	0.977	0.975
HKSynCom33_3	6556180	6416355	0.583	0.574	0.976	0.976
HKSynCom33_4	8651910	8454418	0.831	0.816	0.98	0.969
HKSynCom33_Flg22_1	6075012	5922387	0.887	0.871	0.978	0.964
HKSynCom33_Flg22_2	6675351	6518231	0.555	0.545	0.976	0.975
HKSynCom33_Flg22_3	6949688	6786291	0.72	0.711	0.978	0.975
HKSynCom33_Flg22_4	8763571	8519488	0.624	0.615	0.979	0.976
HKSynCom35_1	5908989	5794978	0.812	0.798	0.978	0.967
HKSynCom35_2	6297490	6170945	0.912	0.9	0.98	0.969
HKSynCom35_3	5647852	5514059	0.73	0.724	0.978	0.977
HKSynCom35_4	5942014	5719804	0.566	0.553	0.972	0.974
HKSynCom35_Flg22_1	6873637	6588694	0.977	0.963	0.977	0.963
HKSynCom35_Flg22_2	6461144	6307990	0.814	0.803	0.979	0.971
HKSynCom35_Flg22_3	8719346	8516934	0.6	0.595	0.978	0.98
HKSynCom35_Flg22_4	8498860	8320269	0.54	0.528	0.979	0.975
Mock_1	8150057	8004876	0.561	0.551	0.981	0.979
Mock_2	7319673	7147533	0.754	0.746	0.977	0.974
Mock_3	5657217	5520195	0.515	0.506	0.976	0.978
Mock_4	8016274	7331269	0.626	0.618	0.973	0.975
Mock_Flg22_1	5753204	5629060	0.706	0.695	0.98	0.974
Mock_Flg22_2	6756972	6609999	0.647	0.639	0.977	0.975
Mock_Flg22_3	6095678	5941698	0.582	0.574	0.976	0.977
Mock_Flg22_4	6114701	5915059	0.801	0.788	0.978	0.968
SynCom33_1	6496075	6185955	0.73	0.72	0.975	0.971
SynCom33_2	6573180	6316721	0.505	0.5	0.975	0.981
SynCom33_3	8633739	8410426	0.671	0.665	0.979	0.978
SynCom33_4	5998570	5741137	0.636	0.626	0.975	0.973
SynCom33_Flg22_1	6049394	5933045	0.673	0.664	0.978	0.974
SynCom33_Flg22_2	6433414	6228966	0.643	0.632	0.977	0.974
SynCom33_Flg22_3	6083498	5921392	0.598	0.591	0.977	0.978
SynCom33_Flg22_4	9369409	9138305	0.671	0.66	0.977	0.972
SynCom35_1	7208495	7066347	0.638	0.625	0.98	0.974
SynCom35_2	6036827	5884003	0.828	0.816	0.978	0.97
SynCom35_3	8767980	8025669	0.802	0.797	0.972	0.972
SynCom35_4	7560214	6873333	0.618	0.608	0.971	0.972
SynCom35_Flg22_1	6224996	6077825	0.617	0.605	0.979	0.975
SynCom35_Flg22_2	6208664	6005766	0.639	0.629	0.976	0.976
SynCom35_Flg22_3	8719287	8476158	0.707	0.698	0.977	0.974
SynCom35_Flg22_4	6892972	6745962	0.691	0.673	0.98	0.968

1stadd re-sequence

sample	rawfq_R1	rawfq_R2	trimfq	H_ath	K_ath	Hvirus_ath	Kvirus_ath
HKSynCom33_1	13172195	13172195	12783484	0.465	0.458	0.975	0.981
HKSynCom33_2	10767044	10767044	10287182	0.652	0.643	0.976	0.975
HKSynCom33_3	10878033	10878033	10560115	0.58	0.572	0.974	0.976
HKSynCom33_4	8651910	8651910	8454418	0.83	0.816	0.98	0.969
HKSynCom33_Flg22_1	6075012	6075012	5922387	0.887	0.871	0.978	0.964
HKSynCom33_Flg22_2	11499848	11499848	11142660	0.552	0.542	0.975	0.975
HKSynCom33_Flg22_3	11608298	11608298	11242512	0.718	0.71	0.976	0.975
HKSynCom33_Flg22_4	8763571	8763571	8519488	0.624	0.615	0.979	0.976
HKSynCom35_1	10084160	10084160	9826277	0.809	0.796	0.976	0.966
HKSynCom35_2	6297490	6297490	6170945	0.912	0.9	0.98	0.969
HKSynCom35_3	9915057	9915057	9603304	0.727	0.722	0.977	0.977
HKSynCom35_4	12587610	12587610	12091544	0.563	0.55	0.971	0.973
HKSynCom35_Flg22_1	6873637	6873637	6588694	0.977	0.963	0.977	0.963
HKSynCom35_Flg22_2	6461144	6461144	6307990	0.814	0.803	0.979	0.971
HKSynCom35_Flg22_3	8719346	8719346	8516934	0.6	0.595	0.977	0.98
HKSynCom35_Flg22_4	12973482	12973482	12618526	0.538	0.526	0.977	0.975
Mock_1	13036385	13036385	12732898	0.559	0.549	0.979	0.978
Mock_2	11495909	11495909	11144820	0.752	0.745	0.976	0.973
Mock_3	14230460	14230460	13715287	0.51	0.503	0.974	0.978
Mock_4	12354390	12354390	11513757	0.592	0.584	0.922	0.922
Mock_Flg22_1	11018794	11018794	10700223	0.703	0.693	0.978	0.973
Mock_Flg22_2	11452528	11452528	11123514	0.644	0.637	0.975	0.975
Mock_Flg22_3	13355598	13355598	12868862	0.579	0.571	0.974	0.977
Mock_Flg22_4	10720255	10720255	10352975	0.8	0.786	0.978	0.968
SynCom33_1	10665251	10665251	10238121	0.729	0.718	0.976	0.971
SynCom33_2	13777849	13777849	13220235	0.502	0.497	0.975	0.981
SynCom33_3	12414664	12414664	12026972	0.668	0.664	0.977	0.978
SynCom33_4	12574124	12574124	12030929	0.634	0.623	0.975	0.972
SynCom33_Flg22_1	11354523	11354523	11037970	0.67	0.661	0.976	0.974
SynCom33_Flg22_2	11098796	11098796	10723034	0.64	0.629	0.977	0.973
SynCom33_Flg22_3	12743113	12743113	12297414	0.536	0.531	0.878	0.881
SynCom33_Flg22_4	9369409	9369409	9138305	0.671	0.66	0.977	0.972
SynCom35_1	11683102	11683102	11384694	0.636	0.623	0.978	0.974
SynCom35_2	10254853	10254853	9911636	0.825	0.814	0.977	0.97
SynCom35_3	8767980	8767980	8025669	0.802	0.797	0.972	0.972
SynCom35_4	12844974	12844974	11942099	0.617	0.606	0.973	0.971
SynCom35_Flg22_1	11048570	11048570	10706105	0.614	0.603	0.977	0.974
SynCom35_Flg22_2	10988696	10988696	10515421	0.635	0.627	0.975	0.975
SynCom35_Flg22_3	8719287	8719287	8476158	0.707	0.698	0.977	0.974
SynCom35_Flg22_4	11436243	11436243	11120876	0.689	0.672	0.979	0.967

5.2 DEGs

• Raw

• Remove sample effect with SVA

• Virus load

• Mock + Mock_flg22

• SynCom33

• SynCom35

• flg22

• SynCom33 + SynCom33_flg22 + SynCom35 + SynCom35_flg22

• mock + mock_flg22 + SynCom33_flg22 + SynCom35_flg22

• 1st RNA-Seq

• 2nd RNA-Seq

5.3 Cluster

  Flg22 SynCom33 LiveSynCom33 SynCom35 LiveSynCom35 LiveBacteria
1      0        0            0        0            0            0
2      1        0            0        0            0            0
3      0        1            0        0            0            0
4      1        1            0        0            0            0
5      0        1            1        0            0            1
6      1        1            1        0            0            1
7      0        0            0        1            0            0
8      1        0            0        1            0            0
9      0        0            0        1            1            1
10     1        0            0        1            1            1

6. Comparison

RawMutant	MapCol0	MapCol0All

MapMutant	RawCol0

• SynCom35 down DEGs

• SynCom35 up DEGs

7 Soil

7.1 Aligned reads

sample	rawfq_R1	rawfq_R2	trimfq	H_ath	K_ath	Hvirus_ath	Kvirus_ath
Mock_1	8146907	8146907	7877992	0.743	0.733	0.967	0.963
Mock_2	18083448	18083448	17391986	0.245	0.241	0.96	0.98
Mock_3	12919098	12919098	12363313	0.468	0.465	0.962	0.974
SynCom33_1	13209898	13209898	12648097	0.406	0.401	0.959	0.97
SynCom33_2	15867598	15867598	15210307	0.277	0.274	0.968	0.986
SynCom33_3	12568370	12568370	12105344	0.437	0.435	0.968	0.98
SynCom35_1	10898141	10898141	10483412	0.652	0.649	0.969	0.975
SynCom35_2	16818587	16818587	16215582	0.341	0.338	0.965	0.98
SynCom35_3	11391744	11391744	10974092	0.57	0.566	0.967	0.973

References

• Garrido-Oter R, Nakano RT, Dombrowski N, Ma KW; AgBiome Team, McHardy AC, Schulze-Lefert P. Modular Traits of the Rhizobiales Root Microbiota and Their Evolutionary Relationship with Symbiotic Rhizobia Cell Host Microbe. 2018;24(1):155-167.e5.