{"type":"doc","content":[{"type":"heading","attrs":{"level":2},"content":[{"text":"Assign reads and otus to samples:","type":"text"}]},{"type":"codeBlock","content":[{"text":"/project/microbiome/data_queue/seq/LowReadII/rawdata\n\nsalloc --account=microbiome -t 0-06:00\n\nmkdir -p /gscratch/grandol1/LowReadII/rawdata\n\ncd /gscratch/grandol1/LowReadII/rawdata\n\nunpigz --to-stdout /project/microbiome/data_queue/seq/LowReadII/rawdata/Low-Read-II_S1_L001_R1_001.fastq | split -l 1000000 -d --suffix-length=3 --additional-suffix=.fastq - LowReadII_R1_ ;unpigz --to-stdout /project/microbiome/data_queue/seq/LowReadII/rawdata/Low-Read-II_S1_L001_R2_001.fastq | split -l 1000000 -d --suffix-length=3 --additional-suffix=.fastq - LowReadII_R2_\n\n//project/microbiome/data_queue/seq/LowReadII/rawdata/run_parse_count_onSplitInput.pl\n\ncd /project/microbiome/data_queue/seq/LowReadII/rawdata\n\n./run_splitFastq_fwd.sh\n\n./run_splitFastq_rev.sh\n\n./run_aggregate.sh\n\ncd /project/microbiome/data_queue/seq/LowReadII/tfmergedreads\n\n./run_slurm_mergereads.pl\n\ncd /project/microbiome/data_queue/seq/LowReadII/otu\n\n./run_slurm_mkotu.pl","type":"text"}]},{"type":"paragraph"},{"type":"paragraph","content":[{"text":"vsearch -sintax OTUFILE -db REFERENCEDATABASE -tabbedout OUTPUT -sintax_cutoff 0.8 -strand both -threads 32","type":"text","marks":[{"type":"strike"}]}]},{"type":"paragraph"},{"type":"paragraph"},{"type":"heading","attrs":{"level":2},"content":[{"text":"Assign taxonomy","type":"text"}]},{"type":"codeBlock","content":[{"text":"salloc --account=microbiome -t 0-02:00 --mem=500G\n\nmodule load swset/2018.05 gcc/7.3.0\n\nmodule load vsearch/2.15.1\n\nvsearch --sintax zotus.fa --db /project/microbiome/users/grandol1/ref_db/gg_16s_13.5.fa -tabbedout LRII.sintax -sintax_cutoff 0.8","type":"text"}]},{"type":"paragraph","content":[{"text":"Output:","type":"text"}]},{"type":"paragraph","content":[{"text":"Reading file /project/microbiome/users/grandol1/ref_db/gg_16s_13.5.fa 100% ","type":"text"}]},{"type":"paragraph","content":[{"text":"1769520677 nt in 1262986 seqs, min 1111, max 2368, avg 1401","type":"text"}]},{"type":"paragraph","content":[{"text":"Counting k-mers 100% ","type":"text"}]},{"type":"paragraph","content":[{"text":"Creating k-mer index 100% ","type":"text"}]},{"type":"paragraph","content":[{"text":"Classifying sequences 100% ","type":"text"}]},{"type":"paragraph","content":[{"text":"Classified 4038 of 4042 sequences (99.90%)","type":"text"}]},{"type":"heading","attrs":{"level":3},"content":[{"text":"Convert into useful form:","type":"text"}]},{"type":"paragraph"},{"type":"codeBlock","content":[{"text":"awk -F \"\\t\" '{OFS=\",\"} NR==1 {print \"OTU_ID\",\"SEQS\",\"SIZE\",\"DOMAIN\",\"KINGDOM\",\"PHYLUM\",\"CLASS\",\"ORDER\",\"FAMILY\",\"GENUS\",\"SPECIES\"} {gsub(\";\", \",\"); gsub(\"centroid=\", \"\"); gsub(\"seqs=\", \"\"); gsub(\"size=\", \"\"); match($4, /d:[^,]+/, d); match($4, /k:[^,]+/, k); match($4, /p:[^,]+/, p); match($4, /c:[^,]+/, c); match($4, /o:[^,]+/, o); match($4, /f:[^,]+/, f); match($4, /g:[^,]+/, g); match($4, /s:[^,]+/, s); print $1, d[0]==\"\" ? \"NA\" : d[0], k[0]==\"\" ? \"NA\" : k[0], p[0]==\"\" ? \"NA\" : p[0], c[0]==\"\" ? \"NA\" : c[0], o[0]==\"\" ? \"NA\" : o[0], f[0]==\"\" ? \"NA\" : f[0], g[0]==\"\" ? \"NA\" : g[0], s[0]==\"\" ? \"NA\" : s[0] }' LRII.sintax > LRIItaxonomy.csv","type":"text"}]},{"type":"paragraph"},{"type":"rule"},{"type":"heading","attrs":{"level":2},"content":[{"text":"LowReadII Analysis","type":"text"}]},{"type":"paragraph","content":[{"text":"This experiment intended to answer the questions below.","type":"text"}]},{"type":"paragraph","content":[{"text":"Questions:","type":"text"}]},{"type":"orderedList","attrs":{"order":1},"content":[{"type":"listItem","content":[{"type":"paragraph","content":[{"text":"How can we best accommodate low concentration input DNAs?","type":"text"}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"text":"Concentrate input DNAs?","type":"text"}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"text":"Concentrate/normalize low yield products based off qPCR?","type":"text"}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"text":"Both?","type":"text"}]}]},{"type":"listItem","content":[{"type":"paragraph","content":[{"text":"Just put more volume of original DNA in?","type":"text"}]}]}]},{"type":"paragraph","content":[{"text":"The questions actually probed are 1, 5, and 3. Though, one could argue that 2 and 5 are similar enough, without doing a cleanup of the DNA as well.","type":"text"}]},{"type":"paragraph","content":[{"text":"Based off 2-step prep sample sequence outputs, we selected 4 average read samples and 8 low read samples. We used our 1-step prep for this experiment though. All samples were normalized to 10 ng/ul or less. 2 ul of each average read sample was used for the 1-step PCR. The low read samples were repeated 3 times at different volumes (2ul, 4ul, and 8ul). 2 ul of 10ng/ul Zymo Mock Community (consisting of Listeria monocytogenes - 12%, Pseudomonas aeruginosa - 12%, Bacillus subtilis - 12%, Escherichia coli - 12%, Salmonella enterica - 12%, Lactobacillus fermentum - 12%, Enterococcus faecalis - 12%, Staphylococcus aureus - 12%, Saccharomyces cerevisiae - 2%, and Cryptococcus neoformans - 2%) was used as a positive control in quadruplicate, and 2 ul of ultra pure water was used as a negative control. After pcr and cleanup, 2 ul from each of these was pooled. All samples have \"_2\", \"_4\", or \"_8\" appended to the sample name to indicate DNA volume used in PCR.","type":"text"}]},{"type":"paragraph","content":[{"text":"The whole above setup was repeated with the same samples with the intention of addressing question 3. After pcr and cleanup, these preps were all quantified via qPCR and various volumes were combined to have each prep equimolar in the pool. These sample names have \"_N\" appended to them for normalized.","type":"text"}]},{"type":"paragraph","content":[{"text":"Both pools were then quantified via qPCR, adjusted to 1 nMolar and combined in equal amounts to maintain 1 nMolar concentration. After dilution to 50pM, this was the pool used for sequencing on the iSeq","type":"text"}]},{"type":"paragraph","content":[{"text":"Read in otu table. Transpose it. Remove added values to front of sample names (mid assignments). Sort by samplename and remove extraneous text, \"centroid=\"","type":"text"}]},{"type":"codeBlock","attrs":{"language":"{r, echo=false,results='hide',fig.keep='all'}"},"content":[{"text":"library(tidyverse)\nlibrary(ggplot2)\n\n#read in otu table\nLowReadOtu <- read.csv(\"/Volumes/Macintosh HD/Users/gregg/Desktop/WorkingFolder/otutable.txt\", sep = \"\", header = TRUE, stringsAsFactors = FALSE)\n\n#LowReadReads <- read.csv(\"/Volumes/Macintosh HD/Users/gregg/Desktop/WorkingFolder/filtermergestats.csv\", header=FALSE, stringsAsFactors = FALSE, na.strings = \"\")\n\n#LowReadReads <- separate(LowReadReads, 1, sep=\"[.]\", into = c(NA, \"samplename\", NA, NA, NA, NA, NA, NA, NA))\n\n#LowReadOtu <- as.data.frame(LowReadOtu)\n\n#transpose\nLowReadOtu<- LowReadOtu %>% \n rownames_to_column() %>% \n gather(variable, value, -rowname) %>% \n spread(rowname, value)\ncolnames(LowReadOtu) <- LowReadOtu[1,]\nLowReadOtu <- LowReadOtu[-1, ] \n\n#simplify naming\nLowReadOtu$OTUID <- gsub(\"rna16S_[A-Z]+_[A-Z]+_\",\"\", LowReadOtu[,1])\n\n\n#Sort by name\nLowReadOtu <- arrange(LowReadOtu, OTUID)\n\n\n#simplify otu names\ncolnames(LowReadOtu) <- gsub(\"centroid=\", \"\", colnames(LowReadOtu))\nLowReadOtu <- type_convert(LowReadOtu)\nLowReadOtu[is.na(LowReadOtu)] <- 0\n\nsubstrRight <- function(x, n){\n substr(x, nchar(x)-n+1, nchar(x))\n}\nLowReadOtu$Normalized <- gsub(\"[0-9]\", \"\", substrRight(LowReadOtu$OTUID, 1))\nLowReadOtu <- arrange(LowReadOtu, Normalized)\nLowReadOtu$Volume <- sub('.*(\\\\d{1}).*', '\\\\1', LowReadOtu$OTUID)\n\n#Total reads assigned to otus\nLowReadOtu <- LowReadOtu %>%\n rowwise()%>%\n mutate(reads = sum(c_across(otu1:otu1967)))\n\n#make sums first observation\nLowReadOtu <- cbind(LowReadOtu[1], LowReadOtu[,3803], LowReadOtu[2:3802])\n#Calculate Percent ISD for all samples\nLowReadOtu$PercISD <- LowReadOtu$otu1 / LowReadOtu$reads\nLowReadOtu <- LowReadOtu[,c(1, 3804, 2:3803)]\n\n#add repeats together maintaining V and N categorizations\nLRIIotu <- LowReadOtu %>%\n group_by(OTUID, Volume, Normalized) %>%\n summarise(TotalReads = sum(reads))\nggplot(LRIIotu, aes(TotalReads, Volume, color=OTUID, shape=Normalized))+\n geom_point(show.legend = FALSE)","type":"text"}]},{"type":"mediaSingle","attrs":{"layout":"center"},"content":[{"type":"media","attrs":{"width":1750,"id":"23ca8477-63d7-4dc8-996a-60b4ca628b6f","collection":"contentId-1852866569","type":"file","height":1081}}]},{"type":"paragraph"},{"type":"paragraph","content":[{"text":"Looking at total reads, qPCR normalization and DNA volume used does not appear to have a consistent impact. Why does there appear to be a disconnect between qPCR results and Reads? We separated out each sample and compared it to itself under the different conditions.","type":"text"}]},{"type":"codeBlock","attrs":{"language":"{r}"},"content":[{"text":"#fix odd naming convention SAG_ vs SAG#\nLRIIotu$OTUID <- gsub(\"SAG_\", \"SAG\", LRIIotu$OTUID)\n#remove V and N appendices from samplenames\nLRIIotu$OTUID <- gsub(\"_.*$\", \"\", LRIIotu$OTUID)\nggplot(LRIIotu[45:72,], aes(TotalReads, Volume, color=OTUID, shape=Normalized))+\n geom_point()+\n facet_wrap(~OTUID)\nggplot(LRIIotu[1:44,], aes(TotalReads, Volume, color=OTUID, shape=Normalized))+\n geom_point()+\n facet_wrap(~OTUID)\nggplot(LRIIotu, aes(TotalReads, Volume, color=OTUID, shape=Normalized))+\n geom_point()+\n facet_wrap(~OTUID)","type":"text"}]},{"type":"mediaSingle","attrs":{"layout":"center"},"content":[{"type":"media","attrs":{"width":1750,"id":"33d15e2e-deac-457d-8a7b-d094faeec4ae","collection":"contentId-1852866569","type":"file","height":1081}}]},{"type":"mediaSingle","attrs":{"layout":"center"},"content":[{"type":"media","attrs":{"width":1750,"id":"789f98f9-a16a-4aa5-8dc0-285aae593b62","collection":"contentId-1852866569","type":"file","height":1081}}]},{"type":"mediaSingle","attrs":{"layout":"center"},"content":[{"type":"media","attrs":{"width":1750,"id":"280e03a1-d11a-4f8b-82c0-3a31339077ee","collection":"contentId-1852866569","type":"file","height":1081}}]},{"type":"paragraph"},{"type":"paragraph","content":[{"text":"There still appears to be a disconnect, though from 2 to 4 ul there is a more consistent increase for the non-normalized. Reads do not consistently go up with increased DNA starting Volume. So, is there a problem with our sample setup? Is our demux key incorrect? Are blanks largely ISD?","type":"text"}]},{"type":"codeBlock","attrs":{"language":"r"},"content":[{"text":"#Explore how the blanks performed\nBlanksOnly <- LowReadOtu[c(1:8, 73:80),] \nlibrary(janitor)\nBlanksOnly <- BlanksOnly %>%\n adorn_totals(\"row\")\nBlanksOnly <- BlanksOnly[which(BlanksOnly[17,] != 0)]\n#Some contamination, but mostly ISD. One replicate of Blank 1_2N is suspect at only 67% ISD.\n#How many samples are more than 65% ISD?\nLowReadOtu$OTUID[which(LowReadOtu$PercISD > 0.65)]","type":"text"}]},{"type":"paragraph","content":[{"text":"[1] \"Blank1_2\" \"Blank1_2\" \"Blank2_2\" \"Blank2_2\" \"Blank3_2\" \"Blank3_2\" \"Blank4_2\" \"Blank4_2\"","type":"text"},{"type":"hardBreak"},{"text":"[9] \"SAG_S30P2R_8\" \"SAG_S30P2R_8\" \"SAG_S38P1R_8\" \"SAG_S38P1R_8\" \"Blank1_2_N\" \"Blank1_2_N\" \"Blank2_2_N\" \"Blank2_2_N\"","type":"text"},{"type":"hardBreak"},{"text":"[17] \"Blank3_2_N\" \"Blank3_2_N\" \"Blank4_2_N\" \"Blank4_2_N\" \"SAG_S15P4R_8_N\" \"SAG_S38P1R_8_N\" \"SAG_S38P1R_8_N\"","type":"text"}]},{"type":"paragraph","content":[{"text":"All of the 8 ul iterations of SAG_S38P1R appear to be largely ISD. All of the samples appearing in the Blank range are 8 ul samples. Does too much DNA or too much inhibitor(s) in DNA favor the ISD?","type":"text"}]},{"type":"paragraph","content":[{"text":"Blanks look good excluding one suspect at only 67% ISD. what about the Mock Community? Is Mock Community largely the 8 expected taxa? (Listeria monocytogenes, Pseudomonas aeruginosa, Bacillus subtilis, Escherichia coli, Salmonella enterica, Lactobacillus fermentum, Enterococcus faecalis, Staphylococcus aureus)","type":"text"}]},{"type":"codeBlock","attrs":{"language":"{r}"},"content":[{"text":"#Read in taxonomy data\nLowReadSyntax <- read.csv(\"/Volumes/Macintosh HD/Users/gregg/Desktop/WorkingFolder/LRIItaxonomy.csv\", header = TRUE, stringsAsFactors = FALSE)\n\n#Simplify table\nLowReadSyntax <- LowReadSyntax[,c(1, 3:11) ]\n\n\n#Is Mock Community largely List mono, Pseu aer, Bac sub, E coli, Sal ent, Lac fer, Ent fae, and Sta aur ??? Isolate MC\nMConly <- LowReadOtu[c(9:16, 81:88),]\n#add totals per otu\nMConly <- MConly %>%\n adorn_totals(\"row\")\n#remove empty otu columns\nMConly <- MConly[which(MConly[17,] != 0)]\n\n#isolate taxonomy data for MC samples only\nMConlyTaxa <- LowReadSyntax[LowReadSyntax$OTU_ID %in% colnames(MConly)[4:65],]\n\n#on a cursory examination, Family seems to be the most consistently called lowest taxonomic level for these samples at 2x151 sequencing.\nMCtaxa <- unique(MConlyTaxa$FAMILY)\nMCtaxa","type":"text"}]},{"type":"paragraph","content":[{"text":"[1] NA \"f:mitochondria\" \"f:Lactobacillaceae\" \"f:Pseudomonadaceae\" \"f:Enterobacteriaceae\"","type":"text"},{"type":"hardBreak"},{"text":"[6] \"f:Bacillaceae\" \"f:Staphylococcaceae\" \"f:Listeriaceae\" \"f:Enterococcaceae\" \"f:[Chthoniobacteraceae]\"","type":"text"}]},{"type":"paragraph","content":[{"text":"So, there are some contaminants that should not be in the Mock Community.","type":"text"}]},{"type":"codeBlock","attrs":{"language":"{r}"},"content":[{"text":"#Mock community bacterial community should be:\n#Listeria monocytogenes - 12%, Pseudomonas aeruginosa - 12%, Bacillus subtilis - 12%, Escherichia coli - 12%, Salmonella enterica - 12%, Lactobacillus fermentum - 12%, Enterococcus faecalis - 12%, Staphylococcus aureus - 12%, Saccharomyces cerevisiae - 2%, and Cryptococcus neoformans - 2%. \n#On the family level this is columns 3 through 9. Salmonella seems to be the least resolved on this iSeq run\n#3 through 9 are the expected families\nMCtaxa <- MCtaxa[3:9]\n\n#isolate the contaminants\nMCcontaminantsTaxa <- subset(MConlyTaxa, !(FAMILY %in% MCtaxa))\n\n#otu1 is not a contaminant; it is ISD. Isolate the others.\nMCcontaminantsTaxa <- MCcontaminantsTaxa[2:9,]\n\n#isolate the relevant contaminant otus\nMCcon <- MCcontaminantsTaxa$OTU_ID\nMCcon","type":"text"}]},{"type":"paragraph","content":[{"text":"[1] \"otu31\" \"otu18\" \"otu51\" \"otu1443\" \"otu1766\" \"otu2540\" \"otu3273\" \"otu3278\"","type":"text","marks":[{"type":"code"}]}]},{"type":"paragraph","content":[{"text":"Are these contaminants present in the blanks? 4 of them are","type":"text"}]},{"type":"codeBlock","attrs":{"language":"{r}"},"content":[{"text":"#Are these contaminants in the blanks? 4 of them are\nMcBlankContaminants <- BlanksOnly[, c(\"OTUID\", \"reads\", \"otu31\", \"otu18\", \"otu51\", \"otu2540\")]","type":"text"}]},{"type":"paragraph","content":[{"text":"The Mock Community appears to be as expected with some contamination. How do the samples look? Could any of them be MC or blanks remembering that some 8 ul DNA samples are showing high ISD.","type":"text"}]},{"type":"codeBlock","attrs":{"language":"{r}"},"content":[{"text":"#Look at only Samples\nSamples <- LowReadOtu[c(17:72, 89:144),]\nSamples <- Samples %>%\n adorn_totals(\"row\")\n#Remove empty columns\nSamples <- Samples[which(Samples[113,] != 0)]\n\n#Cursorily, these are not mostly ISD or MC\n#Lets remove Blank contaminant otus and see\nSamplesnoBlank <- Samples %>%\n select(-one_of(colnames(BlanksOnly)[5:65]))\n#That removes 61 otus. How does it impact read counts? Redo calculations\nSamplesnoBlank <- SamplesnoBlank %>%\n rowwise()%>%\n mutate(NewReads = sum(c_across(otu1:otu1967)))\nSamplesnoBlank <- SamplesnoBlank[,c(1, 3696, 2:3695)] \n#Less than 5% change in read totals removing otus contaminant in blanks\nSamplesnoBlank[113,2]/SamplesnoBlank[113,4]\n","type":"text"}]},{"type":"table","attrs":{"layout":"default","localId":"a84c332c-5f54-414a-9250-24e8e604d8aa"},"content":[{"type":"tableRow","content":[{"type":"tableCell","attrs":{"colspan":1,"rowspan":1,"colwidth":[680.0]},"content":[{"type":"paragraph","content":[{"text":"0.9532478","type":"text"}]}]}]}]},{"type":"paragraph","content":[{"text":"Less than 5% of the reads for the non control samples are from the contaminants present in the blanks. How does this carry over per sample?","type":"text"}]},{"type":"codeBlock","attrs":{"language":"{r}"},"content":[{"text":"#How about per sample? \nSamplesnoBlank$PercDiff <- 1 - (SamplesnoBlank$NewReads/SamplesnoBlank$reads)\nSamplesnoBlank <- SamplesnoBlank[, c(1, 3697, 2:3696)]\nSamplesnoBlank <- arrange(SamplesnoBlank, PercDiff)\n#There are actually 10 samples that are more than ~10% the contaminants from the blanks with the largest at 33%\nPoorSamples <- SamplesnoBlank[104:113,]\nPoorSamples <- arrange(PoorSamples, OTUID)\nPoorSamples[,1:6]","type":"text"}]},{"type":"table","attrs":{"layout":"default","localId":"245f0d0a-646e-4567-a245-5ce60a11a42e"},"content":[{"type":"tableRow","content":[{"type":"tableHeader","attrs":{"colspan":1,"rowspan":1,"colwidth":[113.33]},"content":[{"type":"paragraph","content":[{"text":"OTUID","type":"text"}]},{"type":"paragraph","content":[{"text":"","type":"text"}]}]},{"type":"tableHeader","attrs":{"colspan":1,"rowspan":1,"colwidth":[113.33]},"content":[{"type":"paragraph","content":[{"text":"*PercDiff","type":"text"}]},{"type":"paragraph","content":[{"text":"","type":"text"}]}]},{"type":"tableHeader","attrs":{"colspan":1,"rowspan":1,"colwidth":[113.33]},"content":[{"type":"paragraph","content":[{"text":"NewReads","type":"text"}]},{"type":"paragraph","content":[{"text":"","type":"text"}]}]},{"type":"tableHeader","attrs":{"colspan":1,"rowspan":1,"colwidth":[113.33]},"content":[{"type":"paragraph","content":[{"text":"PercISD","type":"text"}]},{"type":"paragraph","content":[{"text":"","type":"text"}]}]},{"type":"tableHeader","attrs":{"colspan":1,"rowspan":1,"colwidth":[113.33]},"content":[{"type":"paragraph","content":[{"text":"reads","type":"text"}]},{"type":"paragraph","content":[{"text":"","type":"text"}]}]},{"type":"tableHeader","attrs":{"colspan":1,"rowspan":1,"colwidth":[113.33]},"content":[{"type":"paragraph","content":[{"text":"otu1","type":"text"}]},{"type":"paragraph","content":[{"text":"","type":"text"}]}]}]},{"type":"tableRow","content":[{"type":"tableCell","attrs":{"colspan":1,"rowspan":1,"colwidth":[113.33]},"content":[{"type":"paragraph","content":[{"text":"SAG_S30P2R_2","type":"text"}]}]},{"type":"tableCell","attrs":{"colspan":1,"rowspan":1,"colwidth":[113.33]},"content":[{"type":"paragraph","content":[{"text":"0.3068182","type":"text"}]}]},{"type":"tableCell","attrs":{"colspan":1,"rowspan":1,"colwidth":[113.33]},"content":[{"type":"paragraph","content":[{"text":"610","type":"text"}]}]},{"type":"tableCell","attrs":{"colspan":1,"rowspan":1,"colwidth":[113.33]},"content":[{"type":"paragraph","content":[{"text":"0.0750000000","type":"text"}]}]},{"type":"tableCell","attrs":{"colspan":1,"rowspan":1,"colwidth":[113.33]},"content":[{"type":"paragraph","content":[{"text":"880","type":"text"}]}]},{"type":"tableCell","attrs":{"colspan":1,"rowspan":1,"colwidth":[113.33]},"content":[{"type":"paragraph","content":[{"text":"66","type":"text"}]}]}]},{"type":"tableRow","content":[{"type":"tableCell","attrs":{"colspan":1,"rowspan":1,"colwidth":[113.33]},"content":[{"type":"paragraph","content":[{"text":"SAG_S30P2R_4","type":"text"}]}]},{"type":"tableCell","attrs":{"colspan":1,"rowspan":1,"colwidth":[113.33]},"content":[{"type":"paragraph","content":[{"text":"0.1120219","type":"text"}]}]},{"type":"tableCell","attrs":{"colspan":1,"rowspan":1,"colwidth":[113.33]},"content":[{"type":"paragraph","content":[{"text":"650","type":"text"}]}]},{"type":"tableCell","attrs":{"colspan":1,"rowspan":1,"colwidth":[113.33]},"content":[{"type":"paragraph","content":[{"text":"0.3360655738","type":"text"}]}]},{"type":"tableCell","attrs":{"colspan":1,"rowspan":1,"colwidth":[113.33]},"content":[{"type":"paragraph","content":[{"text":"732","type":"text"}]}]},{"type":"tableCell","attrs":{"colspan":1,"rowspan":1,"colwidth":[113.33]},"content":[{"type":"paragraph","content":[{"text":"246","type":"text"}]}]}]},{"type":"tableRow","content":[{"type":"tableCell","attrs":{"colspan":1,"rowspan":1,"colwidth":[113.33]},"content":[{"type":"paragraph","content":[{"text":"SAG_S38P1R_2","type":"text"}]}]},{"type":"tableCell","attrs":{"colspan":1,"rowspan":1,"colwidth":[113.33]},"content":[{"type":"paragraph","content":[{"text":"0.3333333","type":"text"}]}]},{"type":"tableCell","attrs":{"colspan":1,"rowspan":1,"colwidth":[113.33]},"content":[{"type":"paragraph","content":[{"text":"2","type":"text"}]}]},{"type":"tableCell","attrs":{"colspan":1,"rowspan":1,"colwidth":[113.33]},"content":[{"type":"paragraph","content":[{"text":"0.0000000000","type":"text"}]}]},{"type":"tableCell","attrs":{"colspan":1,"rowspan":1,"colwidth":[113.33]},"content":[{"type":"paragraph","content":[{"text":"3","type":"text"}]}]},{"type":"tableCell","attrs":{"colspan":1,"rowspan":1,"colwidth":[113.33]},"content":[{"type":"paragraph","content":[{"text":"0","type":"text"}]}]}]},{"type":"tableRow","content":[{"type":"tableCell","attrs":{"colspan":1,"rowspan":1,"colwidth":[113.33]},"content":[{"type":"paragraph","content":[{"text":"SAG_S44P2R_2","type":"text"}]}]},{"type":"tableCell","attrs":{"colspan":1,"rowspan":1,"colwidth":[113.33]},"content":[{"type":"paragraph","content":[{"text":"0.1930036","type":"text"}]}]},{"type":"tableCell","attrs":{"colspan":1,"rowspan":1,"colwidth":[113.33]},"content":[{"type":"paragraph","content":[{"text":"669","type":"text"}]}]},{"type":"tableCell","attrs":{"colspan":1,"rowspan":1,"colwidth":[113.33]},"content":[{"type":"paragraph","content":[{"text":"0.0024125452","type":"text"}]}]},{"type":"tableCell","attrs":{"colspan":1,"rows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