low proportion of non-chimeric reads

idupanloup · November 14, 2019, 3:29pm

Dear all

I’m puzzled by the low proportion of non-chimeric reads I obtain with the command:
qiime dada2 denoise-paired
–i-demultiplexed-seqs demux-paired-end.qza
–p-trim-left-f 0
–p-trim-left-r 0
–p-trunc-len-f 280
–p-trunc-len-r 220
–o-representative-sequences rep-seqs-dada2.qza
–o-table table-dada2.qza
–output-dir denoising
–p-n-threads 48

denoising_stats.qzv:

sample-id	input	filtered	denoised	merged	non-chimeric
#q2:types	numeric	numeric	numeric	numeric	numeric
1	418763	311787	303312	258323	32586
10	293923	220106	213139	181133	27297
11	306225	233420	223616	185010	29583
12	294613	213471	207708	178502	27723
13	296516	229310	221983	190209	29777
14	339337	263878	255954	219032	25806
15	283101	218357	209309	176372	27013
2	445359	335203	330463	309202	42247
3	512766	383752	379470	354878	51668
4	385325	281007	276781	256989	33575
5	452725	346524	337246	286520	35729
6	466185	357871	347194	293090	31842
7	446120	340125	331603	286642	34744
8	333772	251997	248192	230108	31446
9	257512	194855	187246	154544	25264

Why an i loosing 80% of the reads ?
Best, Isabelle

Nicholas_Bokulich · November 14, 2019, 6:50pm

there could be a high proportion of chimera, but not that high!

Are primers and adapters removed?

You may also want to adjust the min-fold-parent... option described here:

Raising this level has been recommended to reduce false-positive chimera identification:

github.com/benjjneb/dada2

large percentage of merged reads flagged as chimeric, but not due to primers

opened 08:28PM - 05 Nov 18 UTC

closed 08:03PM - 08 Nov 18 UTC

ympiceno

Hello, I'm working with NextSeq 16S data from stool samples. I would like to …track sequences from one group of samples through other samples (donor to recipient), so it is important to recover as many true variants as possible - even if rare - while obtaining the 'cleanest' dataset possible. Our sequencing primers were constructed to not allow sequencing of the primer sequence itself, so my understanding is that we do not need to trim primers or other non-target 'bits' from the sequences. We have paired-end reads of roughly 150 bp each direction. I have sequences run on multiple NS runs, so I'm processing each set through the denoising and merging before concatenating the full set of samples. To test the process, though, I've run some smaller sets through the chimera checking step to see what parameters might be best to use for the rest of the sets. After merging f and r reads, I use the _in-silico_ triming option to have all my merged reads be 252 - 254 bp. I am seeing nearly 90% of the merged sequence variants removed and 20-30% of the merged sequences being removed as chimeric. As the tutorial notes, "Here chimeras make up about 21% of the merged sequence variants, but when we account for the abundances of those variants we see they account for only about 4% of the merged sequence reads", it would appear there are parameters I should change to improve my result. I have used a variety of filterAndTrim settings to test their effects on the number of merged sequences passing the chimera check step (e.g., truncLen = c(150, 140) or no truncation, maxEE = 2,2 or 2,3 ... 2,5, and set matchIDs = TRUE). These have not changed the number of sequences passing the chimera check much overall. I've also tried running the denoising step with no pooling or with pool = TRUE or pool = "pseudo"; again, not much difference. To see if the large number of sequences per sample (e.g., >200K) meant the error learning was being performed on too few samples - and so possibly not modeling error in the full run very well, I increased nbases to 1e+09 in the learnErrors part; no effect that I could discern. For the removeBimera step, I tried all three methods (method = "consensus" - or "pooled" or "per-sample"), again to no real avail. Is it common for adult stool samples with potentially/presumably many sequences of highly related bacteria to yield a large percentage of merged reads as chimeric when processed through DADA2? The extraction blanks I've included have >95% merged reads pass the chimera check, which I presume is because there are few real sequences and the common contaminants observed in seq. data are not highly related (relatively speaking). Should I try altering the OMEGA_A parameter or is this also not likely to have much effect, given what I've tried so far? I'd like to move forward with the rest of the datasets, so if this has been observed previously for adult stool samples - or it seems there are no obvious other things that will likely improve the results post-chimera checking, I will proceed despite the tutorial's caution that "If most of your reads were removed as chimeric, upstream processing may need to be revisited." Thank you for any thoughts/suggestions you may offer.

good luck!

idupanloup · November 19, 2019, 6:19pm

Dear Nicholas
Many thanks for your reply !
Yes, i trimmed my reads and removed primers and adapters (with cutadapt and trimmomatic).
To have more non-chimeric reads, i had to use the following parameter:

 --p-min-fold-parent-over-abundance 4.0

but i’m not sure 4.0 was the best option.
I guess it was: here are my new results:

sample-id	input	filtered	denoised	merged	non-chimeric
#q2:types	numeric	numeric	numeric	numeric	numeric
1	418684	314414	307128	259587	167077
10	293868	221805	215559	180913	131975
11	306157	235094	226474	184649	138192
12	294557	214975	209721	178503	126875
13	296474	231263	224550	190165	145495
14	339261	266235	259151	219141	148347
15	283049	220300	212308	176494	133787
16	264783	202050	196330	167821	124140
17	249265	187381	182673	155767	102374
18	331300	245115	240467	211866	155136
19	471221	351648	345383	305818	216401
2	445266	338176	333812	311272	202944
20	373866	263616	258448	224434	155590
21	489716	374338	368836	327276	218676
22	452017	342898	334917	288588	218418
23	407695	313660	306590	266242	174507
24	331745	246345	241464	214223	151435
25	668362	503044	493842	439425	276127
26	565252	425086	417796	373417	250400
27	434525	326152	320563	290268	200269
28	441632	323068	317846	285004	202334
29	354844	270807	265906	237277	172074
3	512637	386582	382096	357060	229442
30	286781	222626	218440	194471	141057
31	343048	267123	262595	235371	151831
32	295625	222200	217078	188748	130742
4	385248	283269	279509	258170	165668
5	452643	349067	340552	287042	192772
6	466097	360919	351535	293474	183407
7	446053	342994	335302	287738	187609
8	333688	254207	250731	232115	151518
9	257457	196589	189943	154806	115599

Any comment is welcome, if you have some
Best, Isa

Nicholas_Bokulich · November 19, 2019, 6:30pm

I am not sure either! I have not seen a benchmark of these settings (please share if you find one in the lit) but that value appears consistent with the recommendation on the dada2 issue tracker, and sounds reasonable based on the param description. You could try a few different settings to see how it impacts taxonomic composition vs. your expectations.

system · December 21, 2019, 12:30am

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