NestLink 1.14.0
The following content is described in more detail in Egloff et al. (2018), (under review NMETH-A35040).
library(NestLink)library(ExperimentHub)
eh <- ExperimentHub()## snapshotDate(): 2022-10-24query(eh, "NestLink")## ExperimentHub with 8 records
## # snapshotDate(): 2022-10-24
## # $dataprovider: Functional Genomics Center Zurich (FGCZ)
## # $species: NA
## # $rdataclass: data.frame, DNAStringSet
## # additional mcols(): taxonomyid, genome, description,
## # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
## # rdatapath, sourceurl, sourcetype
## # retrieve records with, e.g., 'object[["EH2063"]]'
##
## title
## EH2063 | Sample NGS NB FC linkage data
## EH2064 | Flycodes tryptic digested
## EH2065 | Nanobodies tryptic digested
## EH2066 | FASTA as ground-truth for unit testing
## EH2067 | Known nanobodies
## EH2068 | Quantitaive results for SMEG and COLI
## EH2069 | F255744 Mascot Search result
## EH2070 | WU160118 Mascot Search results# dataFolder <- file.path(path.package(package = 'NestLink'), 'extdata')
# expFile <- list.files(dataFolder, pattern='*.fastq.gz', full.names = TRUE)
expFile <- query(eh, c("NestLink", "NL42_100K.fastq.gz"))[[1]]## see ?NestLink and browseVignettes('NestLink') for documentation## loading from cachescratchFolder <- tempdir()
setwd(scratchFolder)For data QC some known NB were spiked in. Here, we load the NB DNA sequences and translate them to the corresponding AA sequences.
# knownNB_File <- list.files(dataFolder,
# pattern='knownNB.txt', full.names = TRUE)
knownNB_File <- query(eh, c("NestLink", "knownNB.txt"))[[1]]## see ?NestLink and browseVignettes('NestLink') for documentation## loading from cacheknownNB_data <- read.table(knownNB_File, sep='\t',
header = TRUE, row.names = 1, stringsAsFactors = FALSE)
knownNB <- Biostrings::translate(DNAStringSet(knownNB_data$Sequence))
names(knownNB) <- rownames(knownNB_data)
knownNB <- sapply(knownNB, toString)The workflow uses the first 100 reads only for a rapid processing time.
param <- list()
param[['nReads']] <- 100 #Number of Reads from the start of fastq file to process
param[['maxMismatch']] <- 1 #Number of accepted mismatches for all pattern search steps
param[['NB_Linker1']] <- "GGCCggcggGGCC" #Linker Sequence left to nanobody
param[['NB_Linker2']] <- "GCAGGAGGA" #Linker Sequence right to nanobody
param[['ProteaseSite']] <- "TTAGTCCCAAGA" #Sequence next to flycode
param[['FC_Linker']] <- "GGCCaaggaggcCGG" #Linker Sequence next to flycode
param[['knownNB']] <- knownNB
param[['minRelBestHitFreq']] <- 0.8 #minimal fraction of the dominant nanobody for a specific flycode
param[['minConsensusScore']] <- 0.9 #minimal fraction per sequence position in nanabody consensus sequence calculation
param[['minNanobodyLength']] <- 348 #minimal nanobody length in [nt]
param[['minFlycodeLength']] <- 33 #minimal flycode length in [nt]
param[['FCminFreq']] <- 1 #minimal number of subreads for a specific flycode to keep it in the analysisThe following steps are included:
system.time(NB2FC <- runNGSAnalysis(file = expFile[1], param))## user system elapsed
## 2.503 0.256 2.760head(NB2FC, 2)## NB
## 1 SQVQLVESGGGLVQAGGSLRLSCAASGFPVEAHRMYWYRQAPGKEREWVAAISSKGQQTWYADSVKGRFTISRDNAKNTVYLQMNSLKPEDTAVYYCNVKDYGWYYGDYDYWGQGTQVTVS
## 2 SQVQLVESGGGLVQAGGSLRLSCAASGFPVSWTKMYWYRQAPGKEREWVAAIWSTGSWTKYADSVKGRFTISRDNAKNTVYLQMNSLKPEDTAVYYCNVKDKGHQHAHYDYWGQGTQVTVS
## FlycodeCount
## 1 29
## 2 3
## AssociatedFlycodes
## 1 GSAAATAVTWR,GSADGQETDWR,GSADVPEAVWLTVR,GSAPTAPVSWQEGGR,GSAVDPVTVWLTVR,GSDAEGVAAWQSR,GSDAEYTTAWR,GSDDTDETDWR,GSDEAEEEGWQEGGR,GSDPGTDDEWQSR,GSDTEDWEEWQSR,GSDVWDTAVWLTVR,GSEGTDAVGWLTVR,GSEPASEVVWQEGGR,GSEPDVYTAWLTVR,GSEVLDGDEWR,GSFVASFAVWLTVR,GSGDVEGEAWQEGGR,GSGPDPPYGWLR,GSPAVDPPVWLTVR,GSPDEVEVVWLTVR,GSPDSPPAYWLTVR,GSPTVVTFLWR,GSQYTLTPTWLTVR,GSSDAASPSWLTVR,GSTGEDGVVWLTVR,GSTVVTSDPWLTVR,GSVDDQPDTWQEGGR,GSYTPGSTSWQSR
## 2 GSADFPVVAWLR,GSAEVDEADWQEGGR,GSEPDVAAGWQSR
## NB_Name
## 1
## 2head(nanobodyFlycodeLinking.as.fasta(NB2FC))## [1] ">NB0001 FC29 SQVQLVESGGGLVQAGGSLRLSCAASGFPVEAHRMYWYRQAPGKEREWVAAISSKGQQTWYADSVKGRFTISRDNAKNTVYLQMNSLKPEDTAVYYCNVKDYGWYYGDYDYWGQGTQVTVS\nGSAAATAVTWRGSADGQETDWRGSADVPEAVWLTVRGSAPTAPVSWQEGGRGSAVDPVTVWLTVRGSDAEGVAAWQSRGSDAEYTTAWRGSDDTDETDWRGSDEAEEEGWQEGGRGSDPGTDDEWQSRGSDTEDWEEWQSRGSDVWDTAVWLTVRGSEGTDAVGWLTVRGSEPASEVVWQEGGRGSEPDVYTAWLTVRGSEVLDGDEWRGSFVASFAVWLTVRGSGDVEGEAWQEGGRGSGPDPPYGWLRGSPAVDPPVWLTVRGSPDEVEVVWLTVRGSPDSPPAYWLTVRGSPTVVTFLWRGSQYTLTPTWLTVRGSSDAASPSWLTVRGSTGEDGVVWLTVRGSTVVTSDPWLTVRGSVDDQPDTWQEGGRGSYTPGSTSWQSR\n"
## [2] ">NB0002 FC3 SQVQLVESGGGLVQAGGSLRLSCAASGFPVSWTKMYWYRQAPGKEREWVAAIWSTGSWTKYADSVKGRFTISRDNAKNTVYLQMNSLKPEDTAVYYCNVKDKGHQHAHYDYWGQGTQVTVS\nGSADFPVVAWLRGSAEVDEADWQEGGRGSEPDVAAGWQSR\n"
## [3] ">NB0003 FC1 SQVQLVESGGGLVQAGGSLRLSCAASGFPVSWWKMYWYRQAPGKEREWVAAIWSEGWWTKYADSVKGRFTISRDNAKNTVYLQMNSLKPEDTAVYYCNVKDYGGENANYDYWGQGTQVTVS\nGSDGTTEDAWQEGGR\n"
## [4] ">NB0004 FC1 SQVQLVESGGGLVQAGGSLRLSCAASGFPVEWSWMYWYRQAPGKEREWVAAIYSQGRGTEYADSVKGRFTISRDNAKNTVYLQMNSLKPEDTAVYYCNVKDYGWWYGDYDYWGQGTQVTVS\nGSEEAADPAWR\n"
## [5] ">NB0005 FC1 SQVQLVESGGGLVQAGGSLRLSCAASGFPVEAHRMYWYRQAPGKEREWVAAISSKGQQTWYADSVKGRFTISRDNAKNTVYLQMNSLEPEDTAVYYCNVKDYGWYYGDYDYWGQGTQVTVS\nGSEEAEATWWR\n"
## [6] ">NB0006 FC2 SQVQLVESGGGLVQAGGSLRLSCAASGFPVEENFMYWYRQAPGKEREWVAAIYSHGYETEYADSVKGRFTISRDNAKNTVYLQMNSLKPEDTAVYYCNVKDQGYWWWEYDYWGQGTQVTVS\nGSGLPATPAWLRGSTDAEEGVWLTVR\n"To analyze the expressed flycodes mass spectrometry is used.
the FASTA file containing the nanobody - flycode linkage can
be written to a file using functions such as cat.
The exec directory provides alternative shell scripts using command line GNU tools and AWK.
Here is the output of the sessionInfo() command.
## R version 4.2.1 (2022-06-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.5 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.16-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.16-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] scales_1.2.1 ggplot2_3.3.6
## [3] NestLink_1.14.0 ShortRead_1.56.0
## [5] GenomicAlignments_1.34.0 SummarizedExperiment_1.28.0
## [7] Biobase_2.58.0 MatrixGenerics_1.10.0
## [9] matrixStats_0.62.0 Rsamtools_2.14.0
## [11] GenomicRanges_1.50.0 BiocParallel_1.32.0
## [13] protViz_0.7.3 gplots_3.1.3
## [15] Biostrings_2.66.0 GenomeInfoDb_1.34.0
## [17] XVector_0.38.0 IRanges_2.32.0
## [19] S4Vectors_0.36.0 ExperimentHub_2.6.0
## [21] AnnotationHub_3.6.0 BiocFileCache_2.6.0
## [23] dbplyr_2.2.1 BiocGenerics_0.44.0
## [25] BiocStyle_2.26.0
##
## loaded via a namespace (and not attached):
## [1] nlme_3.1-160 bitops_1.0-7
## [3] bit64_4.0.5 RColorBrewer_1.1-3
## [5] filelock_1.0.2 httr_1.4.4
## [7] tools_4.2.1 bslib_0.4.0
## [9] utf8_1.2.2 R6_2.5.1
## [11] KernSmooth_2.23-20 mgcv_1.8-41
## [13] colorspace_2.0-3 DBI_1.1.3
## [15] withr_2.5.0 tidyselect_1.2.0
## [17] bit_4.0.4 curl_4.3.3
## [19] compiler_4.2.1 cli_3.4.1
## [21] DelayedArray_0.24.0 labeling_0.4.2
## [23] bookdown_0.29 sass_0.4.2
## [25] caTools_1.18.2 rappdirs_0.3.3
## [27] stringr_1.4.1 digest_0.6.30
## [29] rmarkdown_2.17 jpeg_0.1-9
## [31] pkgconfig_2.0.3 htmltools_0.5.3
## [33] highr_0.9 fastmap_1.1.0
## [35] rlang_1.0.6 RSQLite_2.2.18
## [37] shiny_1.7.3 farver_2.1.1
## [39] jquerylib_0.1.4 generics_0.1.3
## [41] hwriter_1.3.2.1 jsonlite_1.8.3
## [43] gtools_3.9.3 dplyr_1.0.10
## [45] RCurl_1.98-1.9 magrittr_2.0.3
## [47] GenomeInfoDbData_1.2.9 interp_1.1-3
## [49] Matrix_1.5-1 munsell_0.5.0
## [51] Rcpp_1.0.9 fansi_1.0.3
## [53] lifecycle_1.0.3 stringi_1.7.8
## [55] yaml_2.3.6 zlibbioc_1.44.0
## [57] grid_4.2.1 blob_1.2.3
## [59] parallel_4.2.1 promises_1.2.0.1
## [61] crayon_1.5.2 deldir_1.0-6
## [63] lattice_0.20-45 splines_4.2.1
## [65] KEGGREST_1.38.0 magick_2.7.3
## [67] knitr_1.40 pillar_1.8.1
## [69] codetools_0.2-18 glue_1.6.2
## [71] BiocVersion_3.16.0 evaluate_0.17
## [73] latticeExtra_0.6-30 BiocManager_1.30.19
## [75] png_0.1-7 vctrs_0.5.0
## [77] httpuv_1.6.6 purrr_0.3.5
## [79] gtable_0.3.1 assertthat_0.2.1
## [81] cachem_1.0.6 xfun_0.34
## [83] mime_0.12 xtable_1.8-4
## [85] later_1.3.0 tibble_3.1.8
## [87] AnnotationDbi_1.60.0 memoise_2.0.1
## [89] ellipsis_0.3.2 interactiveDisplayBase_1.36.0Egloff, Pascal, Iwan Zimmermann, Fabian M. Arnold, Cedric A.J. Hutter, Damien Damien Morger, Lennart Opitz, Lucy Poveda, et al. 2018. “Engineered Peptide Barcodes for In-Depth Analyses of Binding Protein Ensembles.” bioRxiv. https://doi.org/10.1101/287813.