Otu table meaning

[syama (syama K)]

Hi,
my fastq file is like this

@SRR9679957.1.1 length=417

TGGGGAATCTTGCGCAATGGGGGGAACCCTGACGCAGCGACGCCGCGTGCGGGACGGAGGCCTTCGGGTCGTAAACCGCTTTCAGCAGGGAAGAGTCAAGACTGTACCTGCCGAAGACGCCCCGGCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGGGGCGAGCGTTATCCGGATTCATTGGGCGTAAAGCGCGCGTAGGCGGCCCGGCAGGCCGGGGGTCGAAGCGGGGGGCTCAACCCCCCGAAGCCCCCGGAACCTCCGCGGCTTGGGTCCGGTAGGGGAGGGTGGAACACCCAGTGTAGCGGTGGAATGCGCAGATATCGGGTGGAACACCGGTGGCGAAGGCGGCCCTCTGGGCCGAGACCGACGCTGAGGCGCGAAAGCTGGGGGAGCGAACAGGATTAGATACCCT
+SRR9679957.1.1 length=417
DFFGGGGGGGF9FEGFGDCFC7FG7FGFDCFFGG:FGGGCGGGGGGGGGDE7F@:@:CEGGGGFGEGGGEE@F8FFGGGGECFGDGFGFGFGGGGFFG9FCFCFFFGF@F6,/*::E8CE>>:CCE?E,37:FGGFGGFFG,8CF*:C=@:E;F>CFGFGE?EED5CCGGGGFGGGGDFEGF#?E::7+2CCGGGEECEG528>GGGGECCEGGCD*DEGEC:58ECFEEGCC?GDE:;F?GGGEEC:FDGF>CEDGFGCEGFCCB<G@8>:GGEEFEFF@FGEGGEFFGDFDCC@FB,5,GFGCDGGGGGGGEGGGFCCFGGF7DEGGFFAGGGGGGGGGGGGGGGGGGGGGGFGGCGGGGFFFCECGGGFGGGGGEGGGGGGGGGGGF:BF>GDGGFAGGGGGGGGGGGGCCCCC

I imported my files using

qiime tools import \
  --input-path sequences/manifestFile.txt \
  --output-path sequences/fj-joined-demux.qza \
  --type 'SampleData[JoinedSequencesWithQuality]' \
  --input-format SingleEndFastqManifestPhred33

Then I did deblur using,

qiime deblur denoise-16S \
  --i-demultiplexed-seqs sequences/fj-joined-demux.qza \
  --p-trim-length 120 \
  --o-representative-sequences sequences/rep-seqs-deblur.qza \
  --o-table sequences/table-deblur.qza \
  --p-sample-stats \
  --o-stats sequences/deblur-stats.qza

After this I could see my table-deblur.qza as,

# Constructed from biom file
#OTU ID	SRR9679959	SRR9679964	SRR9679963	SRR9679962	SRR9680011	SRR9679960	SRR9679961
2e95e8b8c18f2dc58aeab03133078e6f	4708.0	4708.0	4708.0	4708.0	1201.0	4708.0	4708.0
f746b5d43fce71922ad9c6114544728b	2986.0	2986.0	2986.0	2986.0	342.0	2986.0	2986.0
c9e0d41412f0dcd32e26d9359e91e13d	1714.0	1714.0	1714.0	1714.0	1306.0	1714.0	1714.0
024df1584f12933711d059d85490b91b	638.0	638.0	638.0	638.0	372.0	638.0	638.0
8d02588f82e12364d8e72c179057b70d	619.0	619.0	619.0	619.0	0.0	619.0	619.0
0fcac178cc9caab29a1356a8cf715211	568.0	568.0	568.0	568.0	0.0	568.0	568.0
98f2f1aebd963efbebd5ba66ab540a48	532.0	532.0	532.0	532.0	0.0	532.0	532.0
42d5479b183720e25ae65f50c85664dc	501.0	501.0	501.0	501.0	2.0	501.0	501.0
eaa9ffb4838516b5387681e7c189c9bb	400.0	400.0	400.0	400.0	13.0	400.0	400.0
fa9626414e4e76d5ded8f903a722a353	362.0	362.0	362.0	362.0	90.0	362.0	362.0
7fec8a09cd425639a153d83d0ea739e9	322.0	322.0	322.0	322.0	1705.0	322.0	322.0
ebaf90d9564f76f021457b6cc58d129b	308.0	308.0	308.0	308.0	0.0	308.0	308.0
ec0549dc307254906f9d498068d7cd99	230.0	230.0	230.0	230.0	2.0	230.0	230.0
f3173357c011e6d168d5f10510801270	229.0	229.0	229.0	229.0	0.0	229.0	229.0
a3bf54324012f0a2453b59b2dced11c1	203.0	203.0	203.0	203.0	9.0	203.0	203.0
a1d8ded47544628121a6f8476f2ed7e2	201.0	201.0	201.0	201.0	4526.0	201.0	201.0
61f03d4fd814e5479761365746ce995b	200.0	200.0	200.0	200.0	27.0	200.0	200.0
5766c4e6ef1dc538551e5e475cf403bf	193.0	193.0	193.0	193.0	0.0	193.0	193.0
c6e2782d0756921d7fd70ae00caf4b0f	174.0	174.0	174.0	174.0	0.0	174.0	174.0
1a449b37be72a999c5634ac0351ab395	170.0	170.0	170.0	170.0	0.0	170.0	170.0
18c49abac9e3f357f1882ba4007bf281	130.0	130.0	130.0	130.0	3.0	130.0	130.0
aa9e05261dc6fa96e5da2d64a42ea6a1	120.0	120.0	120.0	120.0	0.0	120.0	120.0
0af9a040373c03362c0f43d3badf1fad	114.0	114.0	114.0	114.0	3.0	114.0	114.0
e716622cde49357568a986fac31ecd7b	107.0	107.0	107.0	107.0	0.0	107.0	107.0
c34784e35774f802b6a574097e53295b	104.0	104.0	104.0	104.0	0.0	104.0	104.0
7e52f5e77448ef9c23afb49c51a316cd	100.0	100.0	100.0	100.0	0.0	100.0	100.0
5c0b085a12f13606fc5c75d31e2a9873	93.0	93.0	93.0	93.0	0.0	93.0	93.0
f779d9b0665b6a8b792216a5454352c0	93.0	93.0	93.0	93.0	0.0	93.0	93.0
ed24191353f3ee586ed19baaafaf4e41	92.0	92.0	92.0	92.0	0.0	92.0	92.0
0fefa456be4a0707b66fd40de144d4af	90.0	90.0	90.0	90.0	0.0	90.0	90.0
9c7be040e40806b9c8762aeb91de5fc5	90.0	90.0	90.0	90.0	0.0	90.0	90.0
557224f2e0bdcb29331e46f5dff5a9fb	88.0	88.0	88.0	88.0	0.0	88.0	88.0
3dadcdf0eed46865788c3e72d4bbd658	79.0	79.0	79.0	79.0	0.0	79.0	79.0
c92418463559f3067893a718edada021	79.0	79.0	79.0	79.0	0.0	79.0	79.0
86d09278bc74eee6be2f5ed96a6e457d	74.0	74.0	74.0	74.0	0.0	74.0	74.0
e882a2d1db956e520fe4d3884f62dee4	73.0	73.0	73.0	73.0	0.0	73.0	73.0
599be48a82fe60bf0e5f2d31cf40b32a	68.0	68.0	68.0	68.0	0.0	68.0	68.0
f2da8b404943c31574d9a698dcd0fb4e	67.0	67.0	67.0	67.0	2.0	67.0	67.0
ed38653feaa19f28598800042ec3ad33	65.0	65.0	65.0	65.0	0.0	65.0	65.0
a8529861ba1e8ae50d583342a65f9fbd	61.0	61.0	61.0	61.0	0.0	61.0	61.0
7e331bad12e875df6fd5b292669dd9e6	60.0	60.0	60.0	60.0	0.0	60.0	60.0
497f3754e4668ca362381db6b0aec568	56.0	56.0	56.0	56.0	0.0	56.0	56.0
23b531a18ed0b7805312fd452d1ad83c	55.0	55.0	55.0	55.0	8.0	55.0	55.0
b8c7d4e1e375c385e1a3827289e0d5a8	52.0	52.0	52.0	52.0	1071.0	52.0	52.0
a7b8919d08d794b292986e6aa7acf772	49.0	49.0	49.0	49.0	35.0	49.0	49.0
4f23750dcdc15d1aadb41c8254c88411	47.0	47.0	47.0	47.0	3.0	47.0	47.0
421c326a3862d94a186e791d93c4597e	45.0	45.0	45.0	45.0	0.0	45.0	45.0
9d3ffbae758835c639b5b7df869febbb	42.0	42.0	42.0	42.0	0.0	42.0	42.0
d61409cc3a00577c6afc79b6cf0df998	42.0	42.0	42.0	42.0	172.0	42.0	42.0
8af2a17bbcc0b3a8ec4fa4747cae67c0	39.0	39.0	39.0	39.0	315.0	39.0	39.0
df425ff43f2204d7949e4c14be2e284e	36.0	36.0	36.0	36.0	0.0	36.0	36.0
93a033d250588a24e292d2dbf0b20889	32.0	32.0	32.0	32.0	440.0	32.0	32.0
d1030bac320b057cf61d3c1444d9fec2	32.0	32.0	32.0	32.0	0.0	32.0	32.0
20aa3153dd1edd8e46b41b639fe34ab1	31.0	31.0	31.0	31.0	12.0	31.0	31.0
8e0fcd894e6691abc06ff30944713213	30.0	30.0	30.0	30.0	0.0	30.0	30.0
6bff411c392ad88dfac54093ddeae9fe	28.0	28.0	28.0	28.0	16.0	28.0	28.0
7c5ba01449dce0d95c38cea52ed771d8	27.0	27.0	27.0	27.0	0.0	27.0	27.0
f79a780b54e4718701c86eb5c047ab8b	26.0	26.0	26.0	26.0	29.0	26.0	26.0
9d10c2b0a3e3139ebb6744c387a70255	26.0	26.0	26.0	26.0	0.0	26.0	26.0
8e39febfc56fb000e8f9c3dc461095d5	24.0	24.0	24.0	24.0	0.0	24.0	24.0
0ee12e3c9b769163714306a3512bb236	22.0	22.0	22.0	22.0	58.0	22.0	22.0
eee3ad3ad80d35209a5b05f95d139ecf	19.0	19.0	19.0	19.0	0.0	19.0	19.0
e1b368500f7cd743443ebdbe5fba1f5c	18.0	18.0	18.0	18.0	1029.0	18.0	18.0
a0f0ee4e9c092a819e2c899f6f1dc2b6	17.0	17.0	17.0	17.0	0.0	17.0	17.0
28d01bcad9353096e1b3fb91d16a26f5	15.0	15.0	15.0	15.0	31.0	15.0	15.0
d652c7cb68c0426cf6fddbb1abd018e3	15.0	15.0	15.0	15.0	0.0	15.0	15.0
d8e3ab00c20674fd1d2458b3514b2f1a	15.0	15.0	15.0	15.0	0.0	15.0	15.0
6ea3ab7c64a9456b3728def8e55b81d4	15.0	15.0	15.0	15.0	0.0	15.0	15.0
83274937ad0dcef163e74b2b49e6d9fe	15.0	15.0	15.0	15.0	0.0	15.0	15.0
aa3067109e96f8650ee8e4c3e8978f27	12.0	12.0	12.0	12.0	0.0	12.0	12.0
148fef114366d9a78eff952e01a3fa11	11.0	11.0	11.0	11.0	0.0	11.0	11.0
e55eac88bf2eea1fc0b597e46aefd9dd	11.0	11.0	11.0	11.0	0.0	11.0	11.0
f708bfc067adeb5485733d5c7e0ae5e4	11.0	11.0	11.0	11.0	0.0	11.0	11.0
e2978815ffca2c9c12278a6d63369d61	10.0	10.0	10.0	10.0	2.0	10.0	10.0
e6c95906488a22bfd2145a7d24123c84	10.0	10.0	10.0	10.0	4.0	10.0	10.0
c4e8d549510ea0db3ab54d5de73ba7df	9.0	9.0	9.0	9.0	0.0	9.0	9.0
47636adc7b8bf629812a1847177d309d	9.0	9.0	9.0	9.0	0.0	9.0	9.0
f97e79b6a63b667f1b66c772bbfbdc90	8.0	8.0	8.0	8.0	0.0	8.0	8.0
c2b4b1e45327ed8acd4d54bfe5f24ff9	8.0	8.0	8.0	8.0	0.0	8.0	8.0
386f22eeb370f64995f60b2ae4bf297b	8.0	8.0	8.0	8.0	0.0	8.0	8.0
6f9e55e28eb0381eee1d3a9aff9c9c4b	8.0	8.0	8.0	8.0	2.0	8.0	8.0
a74a451dcd5a97de4a5809e6b6d6b2cc	8.0	8.0	8.0	8.0	0.0	8.0	8.0
c2fd0cb9b451e64e1208ea75bb621bca	7.0	7.0	7.0	7.0	0.0	7.0	7.0
1e50b86dc899b444ac08c005aa876f08	6.0	6.0	6.0	6.0	0.0	6.0	6.0
f032a64827f59e7eea0640ff176fe3e6	6.0	6.0	6.0	6.0	0.0	6.0	6.0
df1f38fa437eb94c70db97108bb7f806	6.0	6.0	6.0	6.0	27.0	6.0	6.0
c9e635d5b5d35573977c2a460b0775f1	5.0	5.0	5.0	5.0	0.0	5.0	5.0
f67242e6a9dbb1db3e3890c3b4a03439	5.0	5.0	5.0	5.0	0.0	5.0	5.0
95ea251f970d180a88955c16fe73236e	5.0	5.0	5.0	5.0	0.0	5.0	5.0
b8821b3e08fdf3eb6a0a34610e893b04	5.0	5.0	5.0	5.0	0.0	5.0	5.0
e664093ed61d595d16e66b0e1a5b2341	5.0	5.0	5.0	5.0	0.0	5.0	5.0
45fba91e9887be2927b0a15e2b7d0988	4.0	4.0	4.0	4.0	0.0	4.0	4.0
4f00051981921c447c7aacbc39f7fa3e	4.0	4.0	4.0	4.0	2.0	4.0	4.0
c182cba5ce41b51d9f71648804067ecd	4.0	4.0	4.0	4.0	0.0	4.0	4.0
b2acf5679f034f6bfa434a7a524b1441	4.0	4.0	4.0	4.0	0.0	4.0	4.0
08260813bcac58698d6fa1aef4b05f2f	4.0	4.0	4.0	4.0	0.0	4.0	4.0
7a4b9001d720b6b0c486f8610d5df6c4	4.0	4.0	4.0	4.0	0.0	4.0	4.0
a7ae82da1b125c6c2a3ed07ae1248178	4.0	4.0	4.0	4.0	1.0	4.0	4.0
ccbe2deab5f53e999311afe6b76912ab	4.0	4.0	4.0	4.0	0.0	4.0	4.0
09570522a7b42dd5009a1f700fc673e7	4.0	4.0	4.0	4.0	0.0	4.0	4.0
ac56ca473e093e599de51d93d8ebdddd	4.0	4.0	4.0	4.0	302.0	4.0	4.0
b0945b2a16197472802e926e7406c5df	3.0	3.0	3.0	3.0	15.0	3.0	3.0
52363da50b856e56679e29938a58b5c1	3.0	3.0	3.0	3.0	32.0	3.0	3.0
fb53d93664d259c286ebbddbd0116cba	3.0	3.0	3.0	3.0	6.0	3.0	3.0
d7b86552d8b63a889702fc8d0b43e2cd	3.0	3.0	3.0	3.0	0.0	3.0	3.0
d3c88618de0284453422bd992bf4187a	2.0	2.0	2.0	2.0	3.0	2.0	2.0
29d0c78bb34aed05e6322feabdd4aad4	2.0	2.0	2.0	2.0	0.0	2.0	2.0
6387845fcbaf0e0af3f182572dce6a16	2.0	2.0	2.0	2.0	0.0	2.0	2.0
fec7272a9908216d0559f868a098c967	2.0	2.0	2.0	2.0	0.0	2.0	2.0
003f3492f48d47adc7332c9a4d635b1e	2.0	2.0	2.0	2.0	6.0	2.0	2.0
5f6c11486c35be779b2b5603d7f07370	2.0	2.0	2.0	2.0	0.0	2.0	2.0
6a1b8bdcfc112c936675bed5947e4e66	2.0	2.0	2.0	2.0	0.0	2.0	2.0
a5f6dd6d269ca41e5f41e38c80476135	2.0	2.0	2.0	2.0	0.0	2.0	2.0
ebf3e3570ae9a6b8f4e47d2fb3e9616e	2.0	2.0	2.0	2.0	3.0	2.0	2.0
8e721d961807358e0eca8b9cfe158e9d	2.0	2.0	2.0	2.0	0.0	2.0	2.0
c954554d35b8ebb18bb9950549aacca6	2.0	2.0	2.0	2.0	0.0	2.0	2.0
f19b1b1f129cd357cff074254e0581f8	2.0	2.0	2.0	2.0	0.0	2.0	2.0

sorry for the long data.

can someone explain what is otuID means.

then I did taxonomy analysis using pre-trained Naive Bayes classifier gg-13-8-99-515-806-nb-classifier.qza, is that the way do taxonomic analysis

qiime feature-classifier classify-sklearn \
  --i-classifier gg-13-8-99-515-806-nb-classifier.qza \
  --i-reads rep-seqs.qza \
  --o-classification taxonomy.qza

then I get the otu table after collapsing taxonomy with table

# Constructed from biom file
#OTU ID	SRR9679959	SRR9679964	SRR9679963	SRR9679962	SRR9680011	SRR9679960	SRR9679961
Unassigned	1865.0	1865.0	1865.0	1865.0	2434.0	1865.0	1865.0
k__Bacteria	16375.0	16375.0	16375.0	16375.0	16681.0	16375.0	16375.0

Is this the correct way of doing taxonomy analysis.

can I use the table-deblur.qza as feature for my machine learning algorithm?

I wanted to use ANN for classifying the sequences, How will I proceed after deblur?

thanks in advance

[syama (syama K)]

Hi,

I wanted to apply machine learning algorithm for classifying sequences. I have done till deblur

#OTU ID	SRR9679959	SRR9679964	SRR9679963	SRR9679962	SRR9680011	SRR9679960	SRR9679961
2e95e8b8c18f2dc58aeab03133078e6f	4708.0	4708.0	4708.0	4708.0	1201.0	4708.0	4708.0
f746b5d43fce71922ad9c6114544728b	2986.0	2986.0	2986.0	2986.0	342.0	2986.0	2986.0
c9e0d41412f0dcd32e26d9359e91e13d	1714.0	1714.0	1714.0	1714.0	1306.0	1714.0	1714.0
024df1584f12933711d059d85490b91b	638.0	638.0	638.0	638.0	372.0	638.0	638.0
8d02588f82e12364d8e72c179057b70d	619.0	619.0	619.0	619.0	0.0	619.0	619.0
0fcac178cc9caab29a1356a8cf715211	568.0	568.0	568.0	568.0	0.0	568.0	568.0
98f2f1aebd963efbebd5ba66ab540a48	532.0	532.0	532.0	532.0	0.0	532.0	532.0
42d5479b183720e25ae65f50c85664dc	501.0	501.0	501.0	501.0	2.0	501.0	501.0
eaa9ffb4838516b5387681e7c189c9bb	400.0	400.0	400.0	400.0	13.0	400.0	400.0
fa9626414e4e76d5ded8f903a722a353	362.0	362.0	362.0	362.0	90.0	362.0	362.0
7fec8a09cd425639a153d83d0ea739e9	322.0	322.0	322.0	322.0	1705.0	322.0	322.0

got table like this

what to do next?

[jwdebelius (Justine Debelius)]

Hi @syama,

There are a couple of issues I (potentially) see before you want to feed your table into a machine learning algorithm. I want to double check that you did quality filter?

Your feature table looks like a feature table. The #OTU ID column gives you the feature name and is the default description for this particular data format.

I am concerned that your taxonomic annotation was bad. Did you only look at kingdom level? Was. the data only classified to kingdom level???

@syama

I wanted to use ANN for classifying the sequences, How will I proceed after deblur?

My suggestion here is to make sure that your processing is correct. I like an unsupervised method (beta diversity) first to look for a difference in your data.

You may also want to think about filtering and how you'd like to handle the compositional nature of the data; Im not as familiar with ANN, but you need someway to address the fact that sequencing depth and biomass are not correlated.

Best,
Justine

PS I combined your two posts because they seemed to be asking similar questions about a similar issue

[syama (syama K)]

Hi @jwdebelius,

Thankyou for the response.

my data is demultiplexed joined data right?

is my import command correct?

when I tried quality-filter,
qiime quality-filter q-score
–i-demux fj-joined-demux.qza
–o-filtered-sequences demux-filtered.qza
–o-filter-stats demux-filter-stats.qza

I got the error Plugin error from quality-filter:

’NoneType’ object has no attribute 'strip’

Debug info has been saved to /var/folders/hr/9vcxtv2961bgx43wk4sqww3m0000gn/T/qiime2-q2cli-err-r19tjaum.log

what would be the reason.

[thermokarst]

@syama - please do not cross-post like this:

error while running QIIME demux summary

Hi, I am new to QIIME. I have a dataset downloaded from SRA, where each folder for each sample. No barcode file. So is the data demultiplexed? This is how the sequences in each file look like, @SRR9680011.1.1 length=441 TGGGGAATATTGCACAATGGGCGCAAGCCTGATGCAGCCATGCCGCGTGTATGAAGAAGGCCTTCGGGTTGTAAAGTACTTTCAGCGGGGAGGAAGGGAGTAAAGTTAATACCTTTGCTCATTGACGTTACACGCAGATGAAGCACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGTGCAAGCGTTAAGCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTGGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCGGGGAACTG…

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