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Additional file 12 of MetaPro: a scalable and reproducible data processing and analysis pipeline for metatranscriptomic investigation of microbial communities
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Taj, Billy; Adeolu, Mobolaji; Xiong, Xuejian; Ang, Jordan; Nursimulu, Nirvana; Parkinson, John — 2024 Additional file 12: Table S8. Computational performance statistics of MetaPro, HUMAnN3, and SAMSA2. This table reports the amount of processing time required for each run of the three pipelines.  MetaPro additionally exports the timing data of each stage independently. HUMAnN3’s pre-processing step is a separate stage using a separate tool called KneadData. SAMSA2 cleans the data in the pipeline, but it is integrated and does not export timing data. https://creativecommons.org/licenses/by/4.0/legalcode
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Additional file 5 of MetaPro: a scalable and reproducible data processing and analysis pipeline for metatranscriptomic investigation of microbial communities
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Taj, Billy; Adeolu, Mobolaji; Xiong, Xuejian; Ang, Jordan; Nursimulu, Nirvana; Parkinson, John — 2024 Additional file 5: Table S1. Summary of Sequence Read Processing for Three Metatranscriptomic Datasets (NOD Mouse gut; Kimchi and Human Oral Biofilm) Processed by HUMAnN3, HUMAnN2, MetaPro and SAMSA2. This table reports the processing results from the four pipelines on samples from three different datasets. HUMAnN2 and HUMAnN3’s preprocessing tool concatenates paired reads into 1 single file and treats them as 2 separate reads. The NOD mouse samples are paired-end data, while the kimchi and human oral biofilm represent single-end sequence datasets. Unlike MetaPro and SAMSA2, HUMAnN3 and HUMAnN2 do not report transcripts but instead group proteins identified in their pipelines into gene families that are reported in the final column. https://creativecommons.org/licenses/by/4.0/legalcode
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Additional file 6 of MetaPro: a scalable and reproducible data processing and analysis pipeline for metatranscriptomic investigation of microbial communities
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Taj, Billy; Adeolu, Mobolaji; Xiong, Xuejian; Ang, Jordan; Nursimulu, Nirvana; Parkinson, John — 2024 Additional file 6: Table S2. Polycistronic read statistics for NOD mouse. This table shows the tally of non-overlapping paired-end reads that were assembled into contigs by MetaPro through rnaSPADes and subsequently annotated into discrete genes by MetaGeneMark. This table also shows the prevalence of polycistronic reads that exist within the data. BWA was used to align the assembled paired-end reads against the genes to identify discordant alignments between the forward and reverse-end read of a pair with the same ID.  This table has seven columns: 1) the sample ID. 2) the sample description. 3) the total number of alignments is the number of alignments of a read to a gene that BWA reported. 4) The total number of pairs is the number of IDs that BWA aligned, be it forward, reverse, or both paired-end reads.  5) The paired-end disagreements column are the number of times a forward-end and reverse-end read had different alignments for each NOD mouse sample.  6) The paired-end agreements column shows the number of times a forward-read and reverse-end read aligned to the same gene.  7)  The percentage of paired-end disagreements, relative to the total number of paired-end reads in the sample. The percentage of disagreements (polycistronic reads) are at-best 0.23%, and at-worst 7.8% of assembled, non-overlapped paired-end reads in the NOD mouse samples. https://creativecommons.org/licenses/by/4.0/legalcode
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Additional file 7 of MetaPro: a scalable and reproducible data processing and analysis pipeline for metatranscriptomic investigation of microbial communities
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Taj, Billy; Adeolu, Mobolaji; Xiong, Xuejian; Ang, Jordan; Nursimulu, Nirvana; Parkinson, John — 2024 Additional file 7: Table S3.Read annotation statistics for NOD mouse datasets from MetaPro, HUMAnN3, HUMAnN2, SAMSA2 compared with the gold standard. This table shows the number of reads in each NOD mouse sample each pipeline assigned to the 8 ASF bacteria: Clostridium ASF356, Clostridium ASF502, Eubacterium plexicaudatum, Firimicutes ASF500, Lactobacillus ASF360, Lactobacillus murinus, Mucisprillim schaedleri, and Parabacteroides ASF519.  Due to the similarity between P. ASF519, and P. goldsteinii, the pipelines will sometimes annotate to P. goldsteinii rather than to P.ASF519.  P. goldsteinii was also a dominant species found within the samples outside of the 8 ASF bacteria.  The expected results were produced by annotating the reads with a reference containing only the 8 ASF, using BWA. https://creativecommons.org/licenses/by/4.0/legalcode
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Additional file 8 of MetaPro: a scalable and reproducible data processing and analysis pipeline for metatranscriptomic investigation of microbial communities
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Taj, Billy; Adeolu, Mobolaji; Xiong, Xuejian; Ang, Jordan; Nursimulu, Nirvana; Parkinson, John — 2024 Additional file 8: Table S4.Read annotation statistics for kimchi fermentation datasets from MetaPro, HUMAnN3, HUMAnN2, SAMSA2, compared with the gold standard. This table shows the number of reads in each kimchi sample, annotated to the expected 5 lactic acid bacteria (LAB) from each pipeline: Leuconostic mesenteroides, Lactobacillus sakei, Weissella koreensis, Leuconostoc carnosum, and Leuconostoc gelidum. The expected results were obtained by annotating the kimchi datasets against a database containing only the reference gene sequences for the 5 LAB, using BWA. https://creativecommons.org/licenses/by/4.0/legalcode
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Additional file 9 of MetaPro: a scalable and reproducible data processing and analysis pipeline for metatranscriptomic investigation of microbial communities
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Taj, Billy; Adeolu, Mobolaji; Xiong, Xuejian; Ang, Jordan; Nursimulu, Nirvana; Parkinson, John — 2024 Additional file 9: Table S5.Comparisons of Enzyme annotations between MetaPro and HUMAnN3 for NOD mouse, kimchi, and human oral biofilm datasets. This table compares the ECs of MetaPro against HUMAnN3 on the NOD mouse, kimchi, and human oral biofilm datasets.  The HUMAnN3 ECs were filtered for EC co-occurrence pairs that were not found in Swiss-Prot, and multiple unique ECs that annotated to the same gene family.  The resulting HUMAnN3 ECs were contrasted against MetaPro’s EC, yielding a common set of ECs found in both tools, ECs found only by MetaPro, and ECs found only by HUMAnN3.  The same comparison is shown for MetaPro’s high-quality EC predictions. https://creativecommons.org/licenses/by/4.0/legalcode
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Additional file 10 of MetaPro: a scalable and reproducible data processing and analysis pipeline for metatranscriptomic investigation of microbial communities
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Taj, Billy; Adeolu, Mobolaji; Xiong, Xuejian; Ang, Jordan; Nursimulu, Nirvana; Parkinson, John — 2024 Additional file 10: Table S6.Comparisons of Enzyme annotations between MetaPro and SAMSA2 for NOD mouse, kimchi, and human oral biofilm datasets. This table compares the ECs of MetaPro against SAMSA2 on the NOD mouse, kimchi, and human oral biofilm datasets.  The SAMSA2 ECs were filtered for EC co-occurrence pairs that were not found in Swiss-Prot, and multiple unique ECs that annotated to the same gene family.  The resulting SAMSA2 ECs were contrasted against MetaPro’s EC, yielding a common set of ECs found in both tools, ECs found only by MetaPro, and ECs found only by SAMSA2. The same comparison is shown for MetaPro’s high-qaulity EC predictions. https://creativecommons.org/licenses/by/4.0/legalcode
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Additional file 11 of MetaPro: a scalable and reproducible data processing and analysis pipeline for metatranscriptomic investigation of microbial communities
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Taj, Billy; Adeolu, Mobolaji; Xiong, Xuejian; Ang, Jordan; Nursimulu, Nirvana; Parkinson, John — 2024 Additional file 11: Table S7.Comparisons of Enzyme annotations between MetaPro and HUMAnN2 for NOD mouse, kimchi, and human oral biofilm datasets. This table compares the ECs of MetaPro against HUMAnN2 on the NOD mouse, kimchi, and human oral biofilm datasets.  The HUMAnN2 ECs were filtered for EC co-occurrence pairs that were not found in Swiss-Prot, and multiple unique ECs that annotated to the same gene family.  The resulting HUMAnN2 ECs were contrasted against MetaPro’s EC, yielding a common set of ECs found in both tools, ECs found only by MetaPro, and ECs found only by HUMAnN2.  The same comparison is shown for MetaPro’s high-qaulity EC predictions. https://creativecommons.org/licenses/by/4.0/legalcode

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