Last updated: 2024-07-27
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 00acf0d | Dave Tang | 2024-07-27 | Join lookup with sample attribute table |
| html | 3876763 | Dave Tang | 2024-07-27 | Build site. |
| Rmd | e6dba7a | Dave Tang | 2024-07-27 | Format sample attribute |
| html | f3646e2 | Dave Tang | 2024-07-27 | Build site. |
| Rmd | 3c6da94 | Dave Tang | 2024-07-27 | Get sample information |
| html | 7e58cae | Dave Tang | 2024-07-25 | Build site. |
| Rmd | a0610af | Dave Tang | 2024-07-25 | Add intro and example query |
| html | 1dbd4f3 | Dave Tang | 2024-07-25 | Build site. |
| Rmd | 39a6fe8 | Dave Tang | 2024-07-25 | Querying the SRA |
The SRAdb package is a compilation of metadata from NCBI SRA and tools. Specifically:
The Sequence Read Archive (SRA) is the largest public repository of sequencing data from the next generation of sequencing platforms including Roche 454 GS System, Illumina Genome Analyzer, Applied Biosystems SOLiD System, Helicos Heliscope, and others. However, finding data of interest can be challenging using current tools.
SRAdb is an attempt to make access to the metadata associated with submission, study, sample, experiment and run much more feasible. This is accomplished by parsing all the NCBI SRA metadata into a SQLite database that can be stored and queried locally. Fulltext search in the package make querying metadata very flexible and powerful.
fastq and sra files can be downloaded for doing alignment locally. Beside ftp protocol, the SRAdb has funcitons supporting fastp protocol (ascp from Aspera Connect) for faster downloading large data files over long distance. The SQLite database is updated regularly as new data is added to SRA and can be downloaded at will for the most up-to-date metadata.
First download SRAmetadb.sqlite.gz and gunzip it; the
function getSRAdbFile() can do this but I recommend
downloading this file externally. The gzipped file is around 6.8G in
size (2024-07-26) and when uncompressed is 138G in size.
sqlfile <- getSRAdbFile(method = "wget")Download database file externally using wget.
wget -c https://gbnci.cancer.gov/backup/SRAmetadb.sqlite.gzPoint to the location of downloaded and gunzipped file.
sqlfile <- '/data2/sradb/SRAmetadb.sqlite'Create a connection for queries. The standard DBI functionality as implemented in RSQLite function dbConnect makes the connection to the database. The dbDisconnect function disconnects the connection.
sra_con <- dbConnect(SQLite(), sqlfile)Database tables.
sra_tables <- dbListTables(sra_con)
sra_tables [1] "col_desc" "experiment" "metaInfo" "run"
[5] "sample" "sra" "sra_ft" "sra_ft_content"
[9] "sra_ft_segdir" "sra_ft_segments" "study" "submission" Study fields.
dbListFields(sra_con, "study") [1] "study_ID" "study_alias" "study_accession"
[4] "study_title" "study_type" "study_abstract"
[7] "broker_name" "center_name" "center_project_name"
[10] "study_description" "related_studies" "primary_study"
[13] "sra_link" "study_url_link" "xref_link"
[16] "study_entrez_link" "ddbj_link" "ena_link"
[19] "study_attribute" "submission_accession" "sradb_updated" Sample fields.
dbListFields(sra_con, "sample") [1] "sample_ID" "sample_alias" "sample_accession"
[4] "broker_name" "center_name" "taxon_id"
[7] "scientific_name" "common_name" "anonymized_name"
[10] "individual_name" "description" "sra_link"
[13] "sample_url_link" "xref_link" "sample_entrez_link"
[16] "ddbj_link" "ena_link" "sample_attribute"
[19] "submission_accession" "sradb_updated" Query.
rs <- dbGetQuery(sra_con, "select * from study limit 3")
rs[, 1:5] study_ID study_alias study_accession
1 1 DRP000001 DRP000001
2 2 DRP000002 DRP000002
3 3 DRP000003 DRP000003
study_title
1 Bacillus subtilis subsp. natto BEST195 genome sequencing project
2 Model organism for prokaryotic cell differentiation and development
3 Comprehensive identification and characterization of the nucleosome structure
study_type
1 Whole Genome Sequencing
2 Whole Genome Sequencing
3 Transcriptome AnalysisQuery matching specific study accession.
dbGetQuery(sra_con, "select * from study where study_accession == 'DRP000001'") study_ID study_alias study_accession
1 1 DRP000001 DRP000001
study_title
1 Bacillus subtilis subsp. natto BEST195 genome sequencing project
study_type
1 Whole Genome Sequencing
study_abstract
1 <b><i>Bacillus subtilis</i> subsp. <i>natto</i> BEST195</b>. i>Bacillus subtilis</i> subsp. <i>natto</i> BEST195 was isolated from fermented soybeans and will be used for comparative genome analysis.
broker_name center_name center_project_name
1 <NA> KEIO Bacillus subtilis subsp. natto BEST195
study_description related_studies primary_study sra_link study_url_link
1 <NA> <NA> <NA> <NA> <NA>
xref_link study_entrez_link ddbj_link ena_link
1 pubmed: 20398357 || pubmed: 25329997 <NA> <NA> <NA>
study_attribute submission_accession sradb_updated
1 <NA> DRA000001 2023-12-03 23:10:21Match a list of study accessions.
rs <- dbGetQuery(sra_con, "select * from study where study_accession in ('DRP000001', 'DRP000003')")
rs[, 1:5] study_ID study_alias study_accession
1 1 DRP000001 DRP000001
2 3 DRP000003 DRP000003
study_title
1 Bacillus subtilis subsp. natto BEST195 genome sequencing project
2 Comprehensive identification and characterization of the nucleosome structure
study_type
1 Whole Genome Sequencing
2 Transcriptome AnalysisHow do we get the SRR accession for SRX510365? (Should be SRR1216135)
exps <- c("SRX510281", "SRX510280", "SRX510279", "SRX510278", "SRX510277", "SRX510276")
lookup <- sraConvert(exps, sra_con = sra_con)
lookup experiment submission study sample run
1 SRX510276 SRA090948 SRP025982 SRS588793 SRR1216046
2 SRX510277 SRA090948 SRP025982 SRS588794 SRR1216047
3 SRX510278 SRA090948 SRP025982 SRS588795 SRR1216048
4 SRX510279 SRA090948 SRP025982 SRS588796 SRR1216049
5 SRX510280 SRA090948 SRP025982 SRS588798 SRR1216050
6 SRX510281 SRA090948 SRP025982 SRS588797 SRR1216051Get sample information.
purrr::map_df(lookup$sample, \(x){
dbGetQuery(sra_con, paste0("select * from sample where sample_accession == '", x, "'"))
}) -> sample_infoThe required sample information is stored in
sample_attribute and that needs to be further
formatted.
sample_info |>
as_tibble() |>
select(sample_accession, sample_attribute) |>
tidyr::separate_longer_delim(cols = sample_attribute, delim = " || ") |>
tidyr::separate_wider_delim(cols = sample_attribute, delim = ": ", names = c('key', 'value')) |>
tidyr::pivot_wider(id_cols = sample_accession, names_from = key, values_from = value) -> sample_attributeJoin into one table.
dplyr::inner_join(x = lookup, y = sample_attribute, by = c('sample' = "sample_accession")) experiment submission study sample run
1 SRX510276 SRA090948 SRP025982 SRS588793 SRR1216046
2 SRX510277 SRA090948 SRP025982 SRS588794 SRR1216047
3 SRX510278 SRA090948 SRP025982 SRS588795 SRR1216048
4 SRX510279 SRA090948 SRP025982 SRS588796 SRR1216049
5 SRX510280 SRA090948 SRP025982 SRS588798 SRR1216050
6 SRX510281 SRA090948 SRP025982 SRS588797 SRR1216051
source_name seqc sample platform
1 Human Brain Reference RNA (HBRR) from Ambion B Illumina HiSeq 2000
2 Human Brain Reference RNA (HBRR) from Ambion B Illumina HiSeq 2000
3 Human Brain Reference RNA (HBRR) from Ambion B Illumina HiSeq 2000
4 Human Brain Reference RNA (HBRR) from Ambion B Illumina HiSeq 2000
5 Human Brain Reference RNA (HBRR) from Ambion B Illumina HiSeq 2000
6 Human Brain Reference RNA (HBRR) from Ambion B Illumina HiSeq 2000
site library id lane barcode flowcell
1 NYG 4 L03 TAGCTT AC132FACXX
2 NYG 4 L04 TAGCTT AC132FACXX
3 NYG 4 L05 TAGCTT AC132FACXX
4 NYG 4 L06 TAGCTT AC132FACXX
5 NYG 4 L07 TAGCTT AC132FACXX
6 NYG 4 L08 TAGCTT AC132FACXXDisconnect.
dbDisconnect(conn = sra_con)
sessionInfo()R version 4.4.0 (2024-04-24)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.4 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[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
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] SRAdb_1.66.0 RCurl_1.98-1.14 graph_1.82.0
[4] BiocGenerics_0.50.0 RSQLite_2.3.7 lubridate_1.9.3
[7] forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4
[10] purrr_1.0.2 readr_2.1.5 tidyr_1.3.1
[13] tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0
[16] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] gtable_0.3.5 xfun_0.44 bslib_0.7.0 processx_3.8.4
[5] Biobase_2.64.0 callr_3.7.6 tzdb_0.4.0 bitops_1.0-7
[9] vctrs_0.6.5 tools_4.4.0 ps_1.7.6 generics_0.1.3
[13] stats4_4.4.0 fansi_1.0.6 blob_1.2.4 pkgconfig_2.0.3
[17] data.table_1.15.4 lifecycle_1.0.4 compiler_4.4.0 git2r_0.33.0
[21] statmod_1.5.0 munsell_0.5.1 getPass_0.2-4 httpuv_1.6.15
[25] htmltools_0.5.8.1 sass_0.4.9 yaml_2.3.8 later_1.3.2
[29] pillar_1.9.0 jquerylib_0.1.4 whisker_0.4.1 limma_3.60.4
[33] cachem_1.1.0 tidyselect_1.2.1 digest_0.6.35 stringi_1.8.4
[37] rprojroot_2.0.4 fastmap_1.2.0 grid_4.4.0 GEOquery_2.72.0
[41] colorspace_2.1-0 cli_3.6.2 magrittr_2.0.3 utf8_1.2.4
[45] withr_3.0.0 scales_1.3.0 promises_1.3.0 bit64_4.0.5
[49] timechange_0.3.0 rmarkdown_2.27 httr_1.4.7 bit_4.0.5
[53] hms_1.1.3 memoise_2.0.1 evaluate_0.24.0 knitr_1.47
[57] rlang_1.1.4 Rcpp_1.0.12 glue_1.7.0 DBI_1.2.3
[61] xml2_1.3.6 rstudioapi_0.16.0 jsonlite_1.8.8 R6_2.5.1
[65] fs_1.6.4