Overview

When you publish manuscripts based on data generated at our facility, we would greatly appreciate an acknowledgement of our efforts. Please cite our facility as follows (for example):

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

We adhere to a general policy for acknowledgements and authorship as established by the Association for Biomolecular Resource Facilities (ABRF) , and we support the following statement from the ABRF.

The existence of core facilities depends in part on proper acknowledgment in publications. This is an important metric of the value of most core facilities. Proper acknowledgment of core facilities enables them to obtain financial and other support so that they may continue to provide their essential services in the best ways possible. It also helps core personnel to advance in their careers, adding to the overall health of the core facility.

Please contact us for assistance in drafting manuscripts.

  • 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.

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 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

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 (version: v0.9.11)

Forward and reverse reads were merged using PEAR.

Figure 1 . Merging results

Table 1 . Sequence merging statistics

Table 1 . Sequence merging statistics
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%

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 (version: 4.4)

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

Table 1 . Trimming statistics
Sample Trim passed Ambig dropped Trim filter dropped Trim length dropped Percent passed Mean Length Mean Trimmed Length Quality trimmed bp
Sample10-M10-Fec2 70366 6 5239 863 92.01% 409.8 373.2 609
Sample11-M12-Fec3 59791 7 4073 451 92.96% 409.8 372.8 611
Sample12-M13-Fec1 64900 5 4370 1900 91.18% 407.5 372.6 739
Sample13-M14-Fec3 59719 4 4661 544 91.98% 409.8 373.0 772
Sample14-M15-Fec1 59675 4 3726 511 93.36% 409.7 372.5 374
Sample15-M16-Fec2 50088 4 3881 2444 88.78% 405.4 372.9 448
Sample16-M18-Fec3 54461 7 3722 632 92.59% 409.8 373.0 926
Sample17-M19-Fec2 86736 7 6180 1892 91.48% 408.0 372.5 361
Sample18-M20-Fec3 58293 6 4975 3339 87.51% 404.2 372.8 1033
Sample19-M23-Fec2 53160 4 3624 1606 91.04% 406.2 372.5 418
Sample20-M24-Fec3 48723 5 3407 6247 83.46% 396.9 372.9 264
Sample21-M25-Fec2 70 0 120 13 34.48% 377.1 372.7 0
Sample22-M26-Fec1 34717 1 2758 12446 69.54% 380.7 373.0 725
Sample23-M29-Fec3 42059 6 3170 5154 83.47% 398.1 372.5 731
Sample24-M30-Fec3 66054 0 4639 2165 90.66% 407.1 373.0 473
Sample25-M32-Fec2 67218 9 4671 6148 86.13% 400.8 372.8 945
Sample26-M33-Fec2 41972 1 3099 2571 88.10% 403.2 372.2 783
Sample27-M34-Fec3 49421 10 3255 3555 87.87% 403.1 372.7 384
Sample28-M35-Fec3 53160 2 3880 438 92.48% 408.9 372.1 907
Sample29-M36-Fec3 52292 6 4317 1393 90.15% 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

Method: VSEARCH reference basedRognes T, Flouri T, Nichols B, Quince C, Mahé F. (2016) VSEARCH: a versatile open source tool for metagenomics. PeerJ 4:e2584. doi: 10.7717/peerj.2584 (version: v2.25.0)

Chimeric sequences were identified using the VSEARCH algorithm as compared with a reference database.

Figure 1 . Chimera checking results

Table 1 . Chimera checking statistics

Table 1 . Chimera checking statistics
Sample Non-chimeras Chimeras Percent passed
Sample10-M10-Fec2 64552 5814 84.41%
Sample11-M12-Fec3 54789 5002 85.18%
Sample12-M13-Fec1 59871 5029 84.12%
Sample13-M14-Fec3 55345 4374 85.24%
Sample14-M15-Fec1 55016 4659 86.08%
Sample15-M16-Fec2 45815 4273 81.21%
Sample16-M18-Fec3 49969 4492 84.95%
Sample17-M19-Fec2 79599 7137 83.95%
Sample18-M20-Fec3 53433 4860 80.21%
Sample19-M23-Fec2 49256 3904 84.35%
Sample20-M24-Fec3 45206 3517 77.43%
Sample21-M25-Fec2 64 6 31.53%
Sample22-M26-Fec1 32454 2263 65.01%
Sample23-M29-Fec3 39398 2661 78.19%
Sample24-M30-Fec3 59564 6490 81.75%
Sample25-M32-Fec2 62442 4776 80.01%
Sample26-M33-Fec2 39424 2548 82.75%
Sample27-M34-Fec3 45957 3464 81.71%
Sample28-M35-Fec3 51176 1984 89.03%
Sample29-M36-Fec3 49230 3062 84.87%

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

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 (version: 1.30.0 )

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

Table 1 . Observation (ASV/OTU) and total sequence counts per sample
Sample Observations Counts
Sample21.M25.Fec2 4 31
Sample22.M26.Fec1 150 31815
Sample23.M29.Fec3 153 38696
Sample26.M33.Fec2 178 38762
Sample20.M24.Fec3 180 44393
Sample15.M16.Fec2 162 44921
Sample19.M23.Fec2 190 48391
Sample16.M18.Fec3 173 49095
Sample18.M20.Fec3 185 52481
Sample14.M15.Fec1 178 54356
Sample13.M14.Fec3 170 54595
Sample11.M12.Fec3 176 54085
Sample12.M13.Fec1 170 58893
Sample24.M30.Fec3 185 58840
Sample25.M32.Fec2 199 61600
Sample10.M10.Fec2 159 63532
Sample17.M19.Fec2 191 78309
Sample27.M34.Fec3 154 45315
Sample28.M35.Fec3 180 50424
Sample29.M36.Fec3 172 48267

Annotate sequences with taxonomic information

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 (version: dada2_taxa.R --version)

Taxonomic annotations were deteremined using a Naive Bayesian approach included in the DADA2 package.

Figure 1 . Summary of major level 2 taxa

Figure 2 . Summary of taxonomic annotation depth

Table 1 . Summary of major level 2 taxa

Table 1 . Summary of major level 2 taxa
Taxon Counts Percent
Bacteria;Bacillota 534830 54.75%
Bacteria;Bacteroidota 432499 44.28%
Bacteria;Deferribacterota 8675 < 1%
Bacteria;Pseudomonadota 407 < 1%
Bacteria;Actinomycetota 390 < 1%
Unassigned 0 0%

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

Table 2 . Summary of taxonomic annotation depth - Grouped by level 2 taxa
Level 2 taxon Level 5 raw counts Level 6 raw counts Level 7 raw counts Level 5 relative counts Level 6 relative counts Level 7 relative counts
Bacillota 17494 503151 14185 1.79% 51.51% 1.45%
Bacteroidota 0 432499 0 - 44.28% -
Deferribacterota 0 0 8675 - - < 1%
Pseudomonadota 0 407 0 - < 1% -
Actinomycetota 0 390 0 - < 1% -

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

Table 1 . Filtering stats
Sample Original Filtered Retained Percent Retained
Sample10.M10.Fec2 63532 0 63532 100.00 %
Sample11.M12.Fec3 54085 0 54085 100.00 %
Sample12.M13.Fec1 58893 0 58893 100.00 %
Sample13.M14.Fec3 54595 0 54595 100.00 %
Sample14.M15.Fec1 54356 0 54356 100.00 %
Sample15.M16.Fec2 44921 0 44921 100.00 %
Sample16.M18.Fec3 49095 0 49095 100.00 %
Sample17.M19.Fec2 78309 0 78309 100.00 %
Sample18.M20.Fec3 52481 0 52481 100.00 %
Sample19.M23.Fec2 48391 2 48389 100.00 %
Sample20.M24.Fec3 44393 0 44393 100.00 %
Sample21.M25.Fec2 31 0 31 100.00 %
Sample22.M26.Fec1 31815 0 31815 100.00 %
Sample23.M29.Fec3 38696 0 38696 100.00 %
Sample24.M30.Fec3 58840 0 58840 100.00 %
Sample25.M32.Fec2 61600 0 61600 100.00 %
Sample26.M33.Fec2 38762 0 38762 100.00 %
Sample27.M34.Fec3 45315 0 45315 100.00 %
Sample28.M35.Fec3 50424 0 50424 100.00 %
Sample29.M36.Fec3 48267 0 48267 100.00 %

Citations