Overview

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Basic processing of the raw data were performed by the University of Illinois at Chicago Research Informatics Core (UICRIC).

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This report provides a high-level summary of the basic bioinformatic analysis included with the amplicon sequencing services provided by the UIC Research Resources Center (RRC). The end result of these bioinformatics services is to provide investigators basic information concerning the abundance of taxa present in the samples. The basic bioinformatic analysis includes basic processing of raw sequence data including read merging, adapter & quality trimming, chimeric checking and processing using DADA2 to generate a table of abundance data and associated taxonomic annotations.

There were 20 samples in this project.

Output Files

File	Description	Type
rep_set_tax_assignments.txt	Taxonomic assignment of master sequences	result
taxa_raw_counts.zip	ZIP archive of taxonomic summaries from phylum to species level result - raw sequence counts	result
taxa_raw_counts.xlsx	Excel spreadsheet of taxonomic summaries from phylum to species level result - raw sequence counts	result
taxa_relative.zip	ZIP archive of taxonomic summaries from phylum to species level result - relative sequence abundance	result
taxa_relative.xlsx	Excel spreadsheet of taxonomic summaries from phylum to species level result - raw sequence counts	result
biom-summary.txt	Summary statistics of ASV table	result
taxa_table.biom	Amplicon Sequence Variant (ASV) table, in BIOM format	result
sequences.zip	ZIP archive of sequences for each sample, after merging, trimming and chimera checking	result
rep_set_sequences.zip	Compressed FASTA file of representative sequences for ASVs	result

Sample listing Download sample list

Sample	OriginalID
Sample10_M10_Fec2	Sample10_M10_Fec2
Sample11_M12_Fec3	Sample11_M12_Fec3
Sample12_M13_Fec1	Sample12_M13_Fec1
Sample13_M14_Fec3	Sample13_M14_Fec3
Sample14_M15_Fec1	Sample14_M15_Fec1
Sample15_M16_Fec2	Sample15_M16_Fec2
Sample16_M18_Fec3	Sample16_M18_Fec3
Sample17_M19_Fec2	Sample17_M19_Fec2
Sample18_M20_Fec3	Sample18_M20_Fec3
Sample19_M23_Fec2	Sample19_M23_Fec2
Sample20_M24_Fec3	Sample20_M24_Fec3
Sample21_M25_Fec2	Sample21_M25_Fec2
Sample22_M26_Fec1	Sample22_M26_Fec1
Sample23_M29_Fec3	Sample23_M29_Fec3
Sample24_M30_Fec3	Sample24_M30_Fec3
Sample25_M32_Fec2	Sample25_M32_Fec2
Sample26_M33_Fec2	Sample26_M33_Fec2
Sample27_M34_Fec3	Sample27_M34_Fec3
Sample28_M35_Fec3	Sample28_M35_Fec3
Sample29_M36_Fec3	Sample29_M36_Fec3

Details

Method: PearJiajie Zhang, Kassian Kobert, Tom Flouri, and Alexandros Stamatakis. (2014) Pear: a fast and accurate illumina paired-end read merger. Bioinformatics, 30(5):614-620: Forward and reverse reads were merged using PEAR.

Figure 1. Merging results

Table 1. Sequence merging statistics Download table data

Sample	Assembled reads	Discarded reads	Not assembled reads	Percent passed
Sample10_M10_Fec2	76474	0	1415	98.18%
Sample11_M12_Fec3	64322	2	777	98.80%
Sample12_M13_Fec1	71175	2	2984	95.97%
Sample13_M14_Fec3	64928	0	1052	98.41%
Sample14_M15_Fec1	63916	0	1485	97.73%
Sample15_M16_Fec2	56417	0	3556	94.07%
Sample16_M18_Fec3	58822	0	1372	97.72%
Sample17_M19_Fec2	94815	0	2222	97.71%
Sample18_M20_Fec3	66613	0	4437	93.76%
Sample19_M23_Fec2	58394	0	1613	97.31%
Sample20_M24_Fec3	58382	0	5214	91.80%
Sample21_M25_Fec2	203	0	11	94.86%
Sample22_M26_Fec1	49922	4	10627	82.44%
Sample23_M29_Fec3	50389	0	5304	90.48%
Sample24_M30_Fec3	72858	0	2391	96.82%
Sample25_M32_Fec2	78046	0	7113	91.65%
Sample26_M33_Fec2	47643	2	3398	93.34%
Sample27_M34_Fec3	56241	0	5758	90.71%
Sample28_M35_Fec3	57480	4	1128	98.07%
Sample29_M36_Fec3	58008	0	2808	95.38%

Details

Method: cutadaptMartin, M. (2011). Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal, 17(1):10-12. doi:https://doi.org/10.14806/ej.17.1.200

Sequencing trimming using cutadapt

Custom Parameters

-a = ^GTGCCAGCMGCCGCGGTAA...AAACTYAAAKRAATTGRCGG$
--trim-n
--max-n = 0
-q = 20
-m = 300
--trimmed-only
-e = 0.10
--report = minimal

Method: Quality trimming

Quality trimming based on quality threshold and length parameters.

Custom Parameters

min length = 300
p = 20

Method: Adapter trimming

Adapter/primer sequences were trimmed from the reads.

Custom Parameters

5' adapter = GTGCCAGCMGCCGCGGTAA
3' adapter = AAACTYAAAKRAATTGRCGG

Method: Adapter filter

Reads that lack the adpater/primer sequences were discarded.

Method: Abiguous nucleotide trimming

Ambiguous nucleotides (N) were trimmed from the ends and reads with internal ambiguous nucleotides were discarded.

Custom Parameters

max-n = 0

Figure 1. Trimming results

Table 1. Trimming statistics Download table data

Sample	Trim passed	Ambig dropped	Trim filter dropped	Trim length dropped	Percent passed	Mean Length	Mean Trimmed Length	Quality trimmed bp
Sample10_M10_Fec2	70388	6	5217	863	92.04%	409.8	373.2	609
Sample11_M12_Fec3	59805	7	4059	451	92.98%	409.8	372.8	611
Sample12_M13_Fec1	64909	5	4361	1900	91.20%	407.5	372.6	739
Sample13_M14_Fec3	59727	4	4653	544	91.99%	409.8	373.0	772
Sample14_M15_Fec1	59679	4	3722	511	93.37%	409.7	372.5	374
Sample15_M16_Fec2	50096	4	3873	2444	88.80%	405.4	372.9	448
Sample16_M18_Fec3	54471	7	3712	632	92.60%	409.8	373.0	926
Sample17_M19_Fec2	86749	7	6167	1892	91.49%	408.0	372.5	361
Sample18_M20_Fec3	58302	6	4966	3339	87.52%	404.2	372.8	1033
Sample19_M23_Fec2	53168	4	3616	1606	91.05%	406.2	372.5	418
Sample20_M24_Fec3	48734	5	3396	6247	83.47%	396.9	372.9	264
Sample21_M25_Fec2	70	0	120	13	34.48%	377.1	372.7	0
Sample22_M26_Fec1	34723	1	2752	12446	69.55%	380.7	373.0	725
Sample23_M29_Fec3	42065	6	3164	5154	83.48%	398.1	372.5	731
Sample24_M30_Fec3	66066	0	4627	2165	90.68%	407.1	373.0	473
Sample25_M32_Fec2	67228	9	4661	6148	86.14%	400.8	372.8	945
Sample26_M33_Fec2	41979	1	3092	2571	88.11%	403.2	372.2	783
Sample27_M34_Fec3	49432	10	3244	3555	87.89%	403.1	372.7	384
Sample28_M35_Fec3	53167	2	3873	438	92.50%	408.9	372.1	907
Sample29_M36_Fec3	52307	6	4302	1393	90.17%	407.0	372.5	878

Chimeric sequences are artifacts of the PCR process and occur when portions of two separate amplicons fuse during the amplification process. The RRC analysis pipeline uses a standard chimera checking program to identify chimeric sequences and the remove from the dataset

Details

Method: USEARCH reference basedRobert C. Edgar. (2010) Search and clustering orders of magnitude faster than blast. Bioinformatics, 26(19):2460-2461: Chimeric sequences were identified using the USEARCH algorithm as compared with a reference database.
Reference sequence database : silva_138.1_16SGlockner FO, Yilmaz P, Quast C, Gerken J, Beccati A, Ciuprina A, Bruns G, Yarza P, Peplies J, Westram R, and Ludwig W. (2017) 25 years of serving the community with ribosomal RNA gene reference databases and tools. Journal of Biotechnology, 261:169-176: Silva v138.1, 16S

Figure 1. Chimera checking results

Table 1. Chimera checking statistics Download table data

Sample	Non-chimeras	Chimeras	Percent passed
Sample10_M10_Fec2	65923	4465	86.20%
Sample11_M12_Fec3	56098	3707	87.21%
Sample12_M13_Fec1	60978	3931	85.67%
Sample13_M14_Fec3	56492	3235	87.01%
Sample14_M15_Fec1	56061	3618	87.71%
Sample15_M16_Fec2	47070	3026	83.43%
Sample16_M18_Fec3	51074	3397	86.83%
Sample17_M19_Fec2	81171	5578	85.61%
Sample18_M20_Fec3	54773	3529	82.23%
Sample19_M23_Fec2	50359	2809	86.24%
Sample20_M24_Fec3	46278	2456	79.27%
Sample21_M25_Fec2	64	6	31.53%
Sample22_M26_Fec1	33102	1621	66.31%
Sample23_M29_Fec3	40097	1968	79.57%
Sample24_M30_Fec3	61430	4636	84.31%
Sample25_M32_Fec2	63827	3401	81.78%
Sample26_M33_Fec2	40006	1973	83.97%
Sample27_M34_Fec3	46583	2849	82.83%
Sample28_M35_Fec3	51697	1470	89.94%
Sample29_M36_Fec3	49863	2444	85.96%

Reduce read count complexity by a number of techniques, e.g. dereplication or sequences clustering.

Details

Method: DADA2 amplicon denoisingCallahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA and Holmes SP (2016). DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods, 13:581-583. doi: 10.1038/nmeth.3869: Amplicon Sequence Variants (ASVs) were infered using DADA2.

Figure 1. Comparison of observation and sequence counts in each sample

Figure 2. Comparison of samples and sequence counts for ASVs/OTUs

Table 1. Observation (ASV/OTU) and total sequence counts per sample Download table data

Sample	Observations	Counts
Sample21_M25_Fec2	4	31
Sample22_M26_Fec1	153	32343
Sample23_M29_Fec3	157	39305
Sample26_M33_Fec2	180	39237
Sample15_M16_Fec2	163	46029
Sample20_M24_Fec3	193	45393
Sample19_M23_Fec2	196	49364
Sample16_M18_Fec3	177	49988
Sample18_M20_Fec3	194	53641
Sample11_M12_Fec3	184	55259
Sample13_M14_Fec3	176	55619
Sample14_M15_Fec1	183	55243
Sample12_M13_Fec1	173	59838
Sample10_M10_Fec2	168	64718
Sample24_M30_Fec3	198	60474
Sample25_M32_Fec2	206	62840
Sample17_M19_Fec2	198	79610
Sample27_M34_Fec3	158	45856
Sample29_M36_Fec3	175	48789
Sample28_M35_Fec3	181	50868

Annotate sequences with taxonomic information

Details

Method: Naive Bayseian classifierCallahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA and Holmes SP (2016). DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods, 13:581-583. doi: 10.1038/nmeth.3869Wang Q, Garrity GM, Tiedje JM, and Cole JR. (2007). Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Applied and environmental microbiology, 73(16):5261-5267. doi:10.1128/AEM.00062-07: Taxonomic annotations were deteremined using a Naive Bayesian approach included in the DADA2 package.
Reference sequence database : silva_138.1_16SGlockner FO, Yilmaz P, Quast C, Gerken J, Beccati A, Ciuprina A, Bruns G, Yarza P, Peplies J, Westram R, and Ludwig W. (2017) 25 years of serving the community with ribosomal RNA gene reference databases and tools. Journal of Biotechnology, 261:169-176: Silva v138.1, 16S

Figure 1. Summary of major level 2 taxa

Figure 2. Summary of taxonomic annotation depth

Table 1. Summary of major level 2 taxa Download table data

Taxon	Counts	Percent
Bacteria;Firmicutes	549249	55.23%
Bacteria;Bacteroidota	435706	43.81%
Bacteria;Deferribacterota	8689	< 1%
Bacteria;Proteobacteria	407	< 1%
Bacteria;Actinobacteriota	392	< 1%
Bacteria;Other	2	< 1%
Unassigned	0	0%

Table 2. Summary of taxonomic annotation depth - Grouped by level 2 taxa Download table data

Level 2 taxon	Level 0 raw counts	Level 4 raw counts	Level 5 raw counts	Level 6 raw counts	Level 7 raw counts	Level 0 relative counts	Level 4 relative counts	Level 5 relative counts	Level 6 relative counts	Level 7 relative counts
Firmicutes	0	66051	150860	310691	21647	-	6.64%	15.17%	31.24%	2.18%
Bacteroidota	0	0	435613	93	0	-	-	43.80%	< 1%	-
Deferribacterota	0	0	0	0	8689	-	-	-	-	< 1%
Proteobacteria	0	0	2	405	0	-	-	< 1%	< 1%	-
Actinobacteriota	0	0	0	392	0	-	-	-	< 1%	-
Unassigned	2	0	0	0	0	< 1%	-	-	-	-

Details

Method: Taxonomic filter

Data are filtered to remove read counts associated with particular taxa

Custom Parameters

filter = c__Chloroplast,f__mitochondria,D_4__Mitochondria,D_3__Chloroplast,Chloroplast,Mitochondria,Synthetic_Rhodanobacter_Spike-In

Method: Relative sequence abundance

Read counts were normalized as fraction of total sequence counts in each sample

Figure 1. Filtering stats

Figure 2. PCA plots of normalized data (relative sequence abundance)

Table 1. Filtering stats Download table data

Sample	Original	Filtered	Retained	Percent Retained
Sample10_M10_Fec2	64718	0	64718	100.00 %
Sample11_M12_Fec3	55259	0	55259	100.00 %
Sample12_M13_Fec1	59838	0	59838	100.00 %
Sample13_M14_Fec3	55619	0	55619	100.00 %
Sample14_M15_Fec1	55243	0	55243	100.00 %
Sample15_M16_Fec2	46029	0	46029	100.00 %
Sample16_M18_Fec3	49988	0	49988	100.00 %
Sample17_M19_Fec2	79610	0	79610	100.00 %
Sample18_M20_Fec3	53641	0	53641	100.00 %
Sample19_M23_Fec2	49364	2	49362	100.00 %
Sample20_M24_Fec3	45393	0	45393	100.00 %
Sample21_M25_Fec2	31	0	31	100.00 %
Sample22_M26_Fec1	32343	0	32343	100.00 %
Sample23_M29_Fec3	39305	0	39305	100.00 %
Sample24_M30_Fec3	60474	0	60474	100.00 %
Sample25_M32_Fec2	62840	0	62840	100.00 %
Sample26_M33_Fec2	39237	0	39237	100.00 %
Sample27_M34_Fec3	45856	0	45856	100.00 %
Sample28_M35_Fec3	50868	0	50868	100.00 %
Sample29_M36_Fec3	48789	0	48789	100.00 %

Overview

Output Files

Read merging

Details

Figure 1. Merging results

Table 1. Sequence merging statistics Download table data

Sequence trimming

Details

Figure 1. Trimming results

Table 1. Trimming statistics Download table data

Chimera checking

Details

Figure 1. Chimera checking results

Table 1. Chimera checking statistics Download table data

Read count simplification

Details

Figure 1. Comparison of observation and sequence counts in each sample

Figure 2. Comparison of samples and sequence counts for ASVs/OTUs

Table 1. Observation (ASV/OTU) and total sequence counts per sample Download table data

Taxonomic annotation

Details

Figure 1. Summary of major level 2 taxa

Figure 2. Summary of taxonomic annotation depth

Table 1. Summary of major level 2 taxa Download table data

Table 2. Summary of taxonomic annotation depth - Grouped by level 2 taxa Download table data

Data normalization

Details

Figure 1. Filtering stats

Figure 2. PCA plots of normalized data (relative sequence abundance)

Table 1. Filtering stats Download table data

Citations