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Commit message:
planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/lfq_protein_quant commit 26ff08776f90f96646598a19cfcf57d42aa4a43b |
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lfq_protein_quant.xml quantitation.r test-data/meta.tab test-data/moff.tab test-data/quantitation.tsv test-data/summarised_proteins.tsv |
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| diff -r 000000000000 -r bb199421f731 lfq_protein_quant.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/lfq_protein_quant.xml Tue Oct 02 16:30:33 2018 -0400 |
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| @@ -0,0 +1,98 @@ +<tool id="lfq_protein_quant" name="Label free protein" version="1.0"> + <description> + summarisation and quantitation + </description> + <requirements> + <requirement type="package" version="2.2.0">bioconductor-msnbase</requirement> + <requirement type="package" version="2.12.0">bioconductor-mzr</requirement> + <requirement type="package" version="1.2.1">r-tidyverse</requirement> + <requirement type="package" version="1.1_18_1">r-lme4</requirement> + <requirement type="package" version="0.1.0">r-furrr</requirement> + <requirement type="package" version="0.7.5">r-msqrob</requirement> + </requirements> + <command detect_errors="exit_code"> + <![CDATA[ + ln -s '${moff_file}' moff.tsv && + ln -s '${meta_file}' meta.tsv && + Rscript '$__tool_directory__/quantitation.r' + moff.tsv + meta.tsv + $only_summarisation + \${GALAXY_SLOTS:-4} + ]]> + </command> + <inputs> + <param name="moff_file" type="data" format="tabular" + label="Peptide summary output from moFF or other peptide quantitation tools" /> + <param name="meta_file" type="data" format="tabular" label="Metadata with sample names and condition" /> + <param name="only_summarisation" type="boolean" truevalue="1" falsevalue="0" + label="Only summarisiation" help="This option will deactivate the quantifiation of data." /> + + </inputs> + <outputs> + <data format="tabular" name="quant" from_work_dir="quantitation.tsv" label="${tool.name} on ${on_string}: quantification"> + <filter>not only_summarisation</filter> + </data> + <data format="tabular" name="summary" from_work_dir="summarised_proteins.tsv" label="${tool.name} on ${on_string}: summary"/> + </outputs> + <tests> + <test> + <param name="moff_file" value="moff.tab"/> + <param name="meta_file" value="meta.tab"/> + <param name="only_summerisation" value="true"/> + <output name="quant" file="quantitation.tsv" /> + <output name="summary" file="summarised_proteins.tsv" /> + </test> + </tests> + <help> + <![CDATA[ +**What it does** + +Protein summarisation and label free quantitation. + +---- + + **Inputs** + + - Quantification input: Tabular file with the summary output from moFF or in case other tools were used for peptide quantitation with the following columns: 'peptides' containing the amino acid sequence; 'prot' with the Uniprot Accession and then one column per sample with the quantitation values. + + :: + + peptides prot sample1 sample2 + AAABDEK B9DM54 1809446 563862 + TELATASDR Q9CFE8 294282 457023 + AMGLATK P85660 194023 428277 + ... + ... + + + - Metadata input: Separate columns with sample names, condition and optionally also lab and machine. Sample names must match exactly the sample names from the peptide quantitation columns in the previous tabular file. + + + :: + + sample condition + sample1 healthy + sample2 disease + ... + ... + + + **Options** + + - Summarisation only: Summarisation is done through robust regression to take also the peptide effect into account. In case only sample column is provided median protein intensity is calculated. + - Summarisation and quantification: Relative quantification is performed according to the information provided in the metadata file. + + + **Outputs** + + - Summarised protein output: Uniprot accession and quantitation values per sample and protein + - Quantification output: Uniprot accession, comparison between conditions, fold change, p-value, q-value + + ]]> + </help> + <citations> + <citation type="doi">10.1074/mcp.M115.055897</citation> + <citation type="doi">10.1093/bioinformatics/btr645</citation> + </citations> +</tool> |
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| diff -r 000000000000 -r bb199421f731 quantitation.r --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/quantitation.r Tue Oct 02 16:30:33 2018 -0400 |
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| b'@@ -0,0 +1,452 @@\n+#################################\n+library(tidyverse)\n+library(furrr)\n+library(lme4)\n+library(MSnbase)\n+library(MSqRob)\n+\n+##Import and preprocess data\n+############################\n+MSnSet2df = function(msnset){\n+ ## Converts Msnset to a tidy dataframe\n+ ## Always creates feature and vector column so these shouldn\'t be defined by user.\n+ ## convenient for downstream analysis steps.\n+ if(any(c("sample", "feature", "expression") %in% c(colnames(fData(msnset)),colnames(pData(msnset))))){\n+ stop("Column names in the \\"fData\\" or \\"pData\\" slot of the \\"msnset\\" object cannot be named\n+ \\"sample\\", \\"feature\\" or \\"expression\\". Please rename these columns before running the analysis.")\n+ }\n+\n+ dt <- as.data.frame(Biobase::exprs(msnset)) %>% mutate(feature = rownames(.)) %>%\n+ gather(sample, expression, - feature, na.rm=TRUE)\n+ dt <- fData(msnset) %>% mutate(feature = rownames(.)) %>% left_join(dt,. , by = \'feature\')\n+ dt <- pData(msnset) %>% mutate(sample = rownames(.)) %>% left_join(dt,. , by = \'sample\')\n+ as_data_frame(dt)\n+}\n+\n+## robust summarisation\n+do_robust_summaristion = function(msnset, group_var = Proteins, keep_fData_cols = NULL, nIter = 20,\n+ sum_fun = summarizeRobust){\n+ ## TODO use funture_map instead of mutate to speed up\n+ ## Uses assumption that featureNames and sampleNames exist in every msnset\n+ ## Can also be used for multiple rounds of normalization, e.g. first from PSMs to peptides, then from peptides to proteins\n+ system.time({## Time how long it takes\n+ group_var <- enquo(group_var) ;#group_var = quo(Proteins)\n+ ## Make tidy dataframe from Msnset\n+ df <- MSnSet2df(msnset)\n+ ## Do summarisision according defined groups\n+ dt <- filter(df, !is.na(expression)) %>% group_by(!!group_var) %>%\n+ mutate(expression = sum_fun(expression, feature, sample, nIter = nIter)) %>%\n+ dplyr::select(!!group_var, sample, expression) %>%\n+ ## collapse to one value per group\n+ distinct\n+ ## Construct an Msnset object from dataframe\n+ dt_exprs <- spread(dt, sample, expression) %>% ungroup\n+ exprs_data <- dplyr::select(dt_exprs, - !!group_var) %>% as.matrix\n+ rownames(exprs_data) <- as.character(pull(dt_exprs, !!group_var))\n+\n+ fd <- dplyr::select(dt_exprs,!!group_var)\n+\n+ ##Select the group variable and all variables you want to keep\n+ if (!is.null(keep_fData_cols)){\n+ fd_ext <- dplyr::select(df, !!group_var, one_of(keep_fData_cols)) %>% distinct\n+ if(nrow(fd)!=nrow(fd_ext)){\n+ stop("Values in the \\"group_var\\" column can only correspond to a single value in the \\"keep_fData_cols\\" column.")\n+ }\n+ fd <- left_join(fd,fd_ext)\n+ }\n+\n+ fd <- as.data.frame(fd)\n+ rownames(fd) <- as.character(pull(fd, !!group_var))\n+ out <- MSnSet(exprs_data, fData = AnnotatedDataFrame(fd) , pData = pData(msnset)[colnames(exprs_data),,drop = FALSE])\n+ }) %>% print\n+ out\n+}\n+\n+summarizeRobust <- function(expression, feature, sample, nIter=100,...) {\n+ ## Assumes that intensities mx are already log-transformed\n+ ## characters are faster to construct and work with then factors\n+ feature <- as.character(feature)\n+ ##If there is only one 1 peptide for all samples return expression of that peptide\n+ if (length(unique(feature)) == 1L) return(expression)\n+ sample <- as.character(sample)\n+ ## modelmatrix breaks on factors with 1 level so make vector of ones (will be intercept)\n+ if (length(unique(sample)) == 1L) sample = rep(1,length(sample))\n+\n+ ## sum contrast on peptide level so sample effect will be mean over all peptides instead of reference level\n+ X = model.matrix(~ -1 + sample + feature,contrasts.arg = list(feature = \'contr.sum\'))\n+ ## MasS::rlm breaks on singulare values.\n+ ## check with base lm if singular values are present.\n+ ## if so, these coefficients will be zero, remove this collumn from model matrix\n+ ## rinse and repeat on reduced modelmatrx till no singular'..b'trace(Hat):\n+ if(squeeze_variance) df_post else df\n+ )\n+ \n+ ## Calculate fold changes and p values for contrast\n+ cat("Estimating p-values contrasts\\n")\n+ df_prot <- df_prot %>%\n+ mutate(contrasts = furrr::future_pmap(list(model = model, contrasts = list(contrasts),\n+ var = var_protein,\n+ df = df_protein, lfc = lfc), contEst)) %>%\n+ ## Calculate qvalues BH\n+ select_at(vars(!!group_vars, contrasts)) %>%\n+ unnest %>% \n+ group_by(contrast) %>%\n+ mutate(qvalue = p.adjust(pvalue, method = p.adjust.method)) %>%\n+ group_by_at(vars(!!group_vars)) %>% nest(.key = contrasts) %>%\n+ left_join(df_prot,.)\n+ }\n+ ) %>% print\n+ if (parallel) stopCluster(cl)\n+ bind_rows(df_prot,df_prot_failed)\n+}\n+\n+read_moff = function(moff,meta){\n+ print(\'START READING MOFF DATA\')\n+ set = readMSnSet2(moff, ecol = -c(1,2),fnames = \'peptide\',\n+ sep = \'\\t\',stringsAsFactors = FALSE)\n+ colnames(fData(set)) = c(\'peptide\',\'protein\')\n+ pd = read_tsv(meta) %>% column_to_rownames(\'sample\') %>% as.data.frame\n+\n+ ## fix msnbase bug 1\n+ ## if there is only 1 sample. Msnbase doesn\'t name it \n+ if (length(sampleNames(set) ==1))\n+ sampleNames(set) = rownames(pd)\n+\n+ pData(set) = pd\n+ ## fix msnbase bug 2\n+ ## bug in msnbase in summarisation (samplenames should be alphabetically)\n+ sample_order = order(sampleNames(set))\n+ set = MSnSet(exprs(set)[,sample_order,drop = FALSE]\n+ , fData = AnnotatedDataFrame(fData(set))\n+ , pData = AnnotatedDataFrame(pData(set)[sample_order,,drop = FALSE]))\n+ print(\'END READING MOFF DATA\')\n+ set\n+}\n+\n+preprocess = function(set){\n+ print(\'START PREPROCESSING\')\n+ if (ncol(set) == 1){\n+ exprs(set)[0 == (exprs(set))] <- NA\n+ set = log(set, base = 2)\n+ ## keep smallest unique groups\n+ groups2 <- smallestUniqueGroups(fData(set)$protein,split = \',\')\n+ sel <- fData(set)$protein %in% groups2\n+ set <- set[sel,]\n+ } else {\n+ ## normalisation\n+ exprs(set)[0 == (exprs(set))] <- NA\n+ set <- normalize(set, \'vsn\')\n+ ## keep smallest unique groups\n+ groups2 <- smallestUniqueGroups(fData(set)$protein,split = \',\')\n+ sel <- fData(set)$protein %in% groups2\n+ set <- set[sel,]\n+ ## remove peptides with less then 2 observations\n+ sel <- rowSums(!is.na(exprs(set))) >= 2\n+ set <- set[sel]\n+ }\n+ print(\'END PREPROCESSING\')\n+ set\n+}\n+\n+summarise = function(set){\n+ print(\'START SUMMARISATION\')\n+ ## Summarisation\n+ if (ncol(set) == 1){\n+ set = combineFeatures(set,fun="median", groupBy = fData(set)$protein,cv = FALSE)\n+ } else {\n+ ## set = combineFeatures(set,fun="robust", groupBy = fData(set)$protein,cv = FALSE)\n+ set = do_robust_summaristion(set,protein)\n+ }\n+ exprs(set) %>% as.data.frame %>% rownames_to_column(\'protein\') %>% write_tsv(\'summarised_proteins.tsv\')\n+ print(\'END SUMMARISATION\')\n+ set\n+}\n+\n+quantify = function(set, cpu = 0){\n+ print(\'START QUANTITATION\')\n+ if ((cpu == 0) | is.na(cpu)) cpu = availableCores()\n+ print(cpu)\n+ form = colnames(pData(set)) %>% paste0(\'(1|\',.,\')\',collapse=\'+\') %>% paste(\'~\',.) %>% as.formula\n+ contrasts <- contrast_helper(form, set, condition)\n+ res = do_mm(formulas = form, set, group_vars = c(protein)\n+ , contrasts = contrasts,type_df = \'traceHat\', parallel = TRUE,cores = cpu) %>%\n+ filter(!map_lgl(contrasts, is.null)) %>%\n+ transmute(protein, contrasts) %>% unnest %>%\n+ transmute(protein\n+ , comparison = str_replace_all(contrast, \'condition\', \'\')\n+ , logFC,pvalue,qvalue) %>%\n+ write_tsv(\'quantitation.tsv\')\n+ print(\'END QUANTITATION\')\n+}\n+\n+args = commandArgs(trailingOnly=TRUE)\n+moff = args[1]\n+meta = args[2]\n+summarise_only = args[3]\n+cpu = strtoi(args[4])\n+\n+res = read_moff(moff, meta) %>%\n+ preprocess %>%\n+ summarise\n+if (summarise_only != 1)\n+ quantify(res, cpu)\n+\n' |
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| diff -r 000000000000 -r bb199421f731 test-data/meta.tab --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/meta.tab Tue Oct 02 16:30:33 2018 -0400 |
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| @@ -0,0 +1,9 @@ +sample condition +sumIntensity_HEK.Ecoli_7.1_replicate1 ecoli_7 +sumIntensity_HEK.Ecoli_3.1_replicate2 ecoli_3 +sumIntensity_HEK.Ecoli_7.1_replicate2 ecoli_7 +sumIntensity_HEK.Ecoli_20.1_replicate2 ecoli_2 +sumIntensity_HEK.Ecoli_20.1_replicate1 ecoli_2 +sumIntensity_HEK.Ecoli_10.1_replicate1 ecoli_1 +sumIntensity_HEK.Ecoli_10.1_replicate2 ecoli_1 +sumIntensity_HEK.Ecoli_3.1_replicate1 ecoli_3 |
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| diff -r 000000000000 -r bb199421f731 test-data/moff.tab --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/moff.tab Tue Oct 02 16:30:33 2018 -0400 |
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| b'@@ -0,0 +1,226 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|
| b |
| diff -r 000000000000 -r bb199421f731 test-data/quantitation.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/quantitation.tsv Tue Oct 02 16:30:33 2018 -0400 |
| b |
| b'@@ -0,0 +1,1130 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592296279654\t1\n+Q9UQ35\tecoli_7-ecoli_3\t0.15347054544585326\t0.16873380848021388\t0.6378137960552085\n+Q9UQ80\tecoli_2-ecoli_1\t-3.245058062541264e-22\t0.9999999999997151\t1\n+Q9UQ80\tecoli_3-ecoli_1\t-1.622432239348533e-21\t0.999999999998576\t1\n+Q9UQ80\tecoli_7-ecoli_1\t-8.406052653030093e-22\t0.9999999999992621\t1\n+Q9UQ80\tecoli_3-ecoli_2\t-1.2979264330944066e-21\t0.9999999999988609\t1\n+Q9UQ80\tecoli_7-ecoli_2\t-5.160994590488828e-22\t0.999999999999547\t1\n+Q9UQ80\tecoli_7-ecoli_3\t7.818269740455238e-22\t0.9999999999993139\t1\n+Q9Y277\tecoli_2-ecoli_1\t0.14640703140398026\t0.19920733163596366\t1\n+Q9Y277\tecoli_3-ecoli_1\t0.07161681540455944\t0.5109170330942586\t1\n+Q9Y277\tecoli_7-ecoli_1\t0.15187186079560547\t0.18479820718899417\t1\n+Q9Y277\tecoli_3-ecoli_2\t-0.0747902159994208\t0.49297996527288\t1\n+Q9Y277\tecoli_7-ecoli_2\t0.005464829391625216\t0.959351638113686\t1\n+Q9Y277\tecoli_7-ecoli_3\t0.08025504539104603\t0.463070159649712\t1\n+Q9Y3F4\tecoli_2-ecoli_1\t9.718799360682765e-19\t0.9999999990893496\t1\n+Q9Y3F4\tecoli_3-ecoli_1\t-9.470784099794427e-19\t0.9999999991125886\t1\n+Q9Y3F4\tecoli_7-ecoli_1\t2.5065978572661926e-18\t0.9999999976513207\t1\n+Q9Y3F4\tecoli_3-ecoli_2\t-1.9189583460477194e-18\t0.9999999982019383\t1\n+Q9Y3F4\tecoli_7-ecoli_2\t1.5347179211979163e-18\t0.9999999985619711\t1\n+Q9Y3F4\tecoli_7-ecoli_3\t3.4536762672456357e-18\t0.9999999967639094\t1\n+Q9Y490\tecoli_2-ecoli_1\t0\t1\t1\n+Q9Y490\tecoli_3-ecoli_1\t0\t1\t1\n+Q9Y490\tecoli_7-ecoli_1\t0\t1\t1\n+Q9Y490\tecoli_3-ecoli_2\t0\t1\t1\n+Q9Y490\tecoli_7-ecoli_2\t0\t1\t1\n+Q9Y490\tecoli_7-ecoli_3\t0\t1\t1\n+Q9Y493\tecoli_2-ecoli_1\t-5.621190015446885e-20\t0.999999999907367\t1\n+Q9Y493\tecoli_3-ecoli_1\t-1.977877357361006e-21\t0.9999999999967406\t1\n+Q9Y493\tecoli_7-ecoli_1\t-1.0440841581793848e-19\t0.9999999998279429\t1\n+Q9Y493\tecoli_3-ecoli_2\t5.423402279710784e-20\t0.9999999999106264\t1\n+Q9Y493\tecoli_7-ecoli_2\t-4.819651566346965e-20\t0.9999999999205758\t1\n+Q9Y493\tecoli_7-ecoli_3\t-1.0243053846057748e-19\t0.9999999998312022\t1\n+Q9Y4L1\tecoli_2-ecoli_1\t5.731449628343196e-22\t0.999999999999222\t1\n+Q9Y4L1\tecoli_3-ecoli_1\t5.650050391201009e-22\t0.9999999999992331\t1\n+Q9Y4L1\tecoli_7-ecoli_1\t5.596504597081475e-22\t0.9999999999992403\t1\n+Q9Y4L1\tecoli_3-ecoli_2\t-8.139923714218677e-24\t0.9999999999999889\t1\n+Q9Y4L1\tecoli_7-ecoli_2\t-1.349450312617206e-23\t0.9999999999999817\t1\n+Q9Y4L1\tecoli_7-ecoli_3\t-5.354579411953383e-24\t0.9999999999999927\t1\n+Q9Y520\tecoli_2-ecoli_1\t-7.0287718444195046e-15\t0.9999999121040615\t1\n+Q9Y520\tecoli_3-ecoli_1\t-2.290970576610039e-14\t0.999999713510392\t1\n+Q9Y520\tecoli_7-ecoli_1\t-2.0051157417078776e-14\t0.9999997492570054\t1\n+Q9Y520\tecoli_3-ecoli_2\t-1.5880933921680886e-14\t0.9999998014063305\t1\n+Q9Y520\tecoli_7-ecoli_2\t-1.3022385572659273e-14\t0.9999998371529439\t1\n+Q9Y520\tecoli_7-ecoli_3\t2.8585483490216146e-15\t0.9999999642533866\t1\n+Q9Y5B9\tecoli_2-ecoli_1\t0.005615071799879606\t0.8853387834778575\t1\n+Q9Y5B9\tecoli_3-ecoli_1\t-0.0015531492547019557\t0.968164582586059\t1\n+Q9Y5B9\tecoli_7-ecoli_1\t-0.004969573956498814\t0.8984304177816331\t1\n+Q9Y5B9\tecoli_3-ecoli_2\t-0.007168221054581562\t0.8539972324244992\t1\n+Q9Y5B9\tecoli_7-ecoli_2\t-0.01058464575637842\t0.7860801787908229\t1\n+Q9Y5B9\tecoli_7-ecoli_3\t-0.0034164247017968584\t0.9300563795409404\t1\n+P55263\tecoli_2-ecoli_1\t0\t1\t1\n+P55263\tecoli_3-ecoli_1\t0\t1\t1\n+P55263\tecoli_7-ecoli_1\t0\t1\t1\n+P55263\tecoli_3-ecoli_2\t0\t1\t1\n+P55263\tecoli_7-ecoli_2\t0\t1\t1\n+P55263\tecoli_7-ecoli_3\t0\t1\t1\n+P0A6Z3\tecoli_7-ecoli_3\t-0.40906290318649974\t0.062027760478629304\t0.3977915677631447\n' |
| b |
| diff -r 000000000000 -r bb199421f731 test-data/summarised_proteins.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/summarised_proteins.tsv Tue Oct 02 16:30:33 2018 -0400 |
| b |
| b'@@ -0,0 +1,190 @@\n+protein\tsumIntensity_HEK.Ecoli_10.1_replicate1\tsumIntensity_HEK.Ecoli_10.1_replicate2\tsumIntensity_HEK.Ecoli_20.1_replicate1\tsumIntensity_HEK.Ecoli_20.1_replicate2\tsumIntensity_HEK.Ecoli_3.1_replicate1\tsumIntensity_HEK.Ecoli_3.1_replicate2\tsumIntensity_HEK.Ecoli_7.1_replicate1\tsumIntensity_HEK.Ecoli_7.1_replicate2\n+A1L0T0\t17.429399406156225\t17.33398240348946\t17.410541528940552\t17.169264537725507\t16.96506289630559\t17.102898046807514\t16.810637234848606\t17.4312146548945\n+O00139\t20.421077251892928\t19.4956589466802\t18.682046032353977\t20.423674084830257\t21.718035972736907\t21.77141317029518\t20.840274733873557\t20.12149078546466\n+O00151\t17.81884023410664\t17.531832922589587\t17.62163842515729\t18.09366919995733\t17.67254437376755\t17.427616936505473\t17.96961117579886\t17.425397847613464\n+O00273\t19.93242622242833\t19.603203040939114\t19.886945840734857\t19.867923201248434\t19.67920363460759\t18.960655886217342\t19.507297177954843\t19.82377306133697\n+O14744\t18.77855154110734\t18.914172184222327\t18.870002449107787\t19.08828557498425\t18.714199271576717\t18.716410902401307\t18.88067875222483\t18.670156314253507\n+O15042\t18.319942988998132\t18.41974317713256\t18.632724745212094\t18.567487062998904\t18.454088773488962\t18.46057387031008\t18.357032366321995\t18.49783796402044\n+O60784\t17.992423465315497\t18.276485111155022\t17.87549658995278\t18.230718126797623\t17.639386766223552\t18.40092918614786\t18.262567273424093\t17.92554141877697\n+O75367\t19.90080428618561\t20.086975161741446\t19.92311091280165\t20.106770427549396\t19.74046206407978\t21.042276581142772\t19.99708611108673\t20.216996219641395\n+O75390\t17.22571077073124\t14.365033325121281\t16.355939065329956\t14.361154531483919\t16.14733168134587\t17.262912073475782\t16.914471388538047\t15.532376815271153\n+O75533\t17.358807786199613\t17.513044498868947\t17.770183697285013\t17.804804873091328\t17.397513206265078\t17.16052774872587\t17.399796896788956\t17.391955819380776\n+O75643\t18.687147078461912\t18.464501804013604\t18.9319587430068\t18.424539180514156\t18.230533239881822\t18.412031679936355\t18.656158664410608\t18.27259240580123\n+O94826\t20.816648764635488\t21.108362470061817\t21.22439537837221\t21.037439062994718\t20.623862901050362\t20.637885957511678\t20.907828637962673\t20.946104482850195\n+O94906\t19.86658386516669\t19.6225520815111\t19.71940800574136\t19.80282982980582\t20.155700892782455\t20.23833594168983\t19.875015585018765\t19.799828402785874\n+O95831\t19.368393014683498\t19.478356880357524\t19.435540943580897\t19.49200043099873\t19.183100831156857\t19.321179939342525\t19.380400911298256\t19.21652294914462\n+P00350\t17.445982845969972\t17.258215460666293\t12.824996608706163\t16.7727933274694\t18.53500506497499\t18.678710595548726\t18.01999606718685\t16.96248073253443\n+P00961, 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|