1000Bulls mapping pipeline at ABGC: Difference between revisions
No edit summary |
No edit summary |
||
| Line 1: | Line 1: | ||
The 1000 Bulls mapping pipeline as currently implemented at ABGC is an extension of a generic pipeline developed for pig at ABGC. Modifications include use of bwa 5.9 in stead of later versions, as per the requirements of the 1000 Bulls consortium. Furthermore, the script will automate setting metadata in the BAM files such as including the official 1000 Bulls Ids of the individual cows. Another modification is the use of an sqlite-based database that holds some the metadata required for performing the mapping and setting the correct ids. The sqlite database should be called 'cow_schema.db' and should be in the same working directory as the Python3 master script. Currently, the following two tables should be present in the database: <code>cow_schema_main</code> and <code>bulls1K_id</code> | The 1000 Bulls mapping pipeline as currently implemented at ABGC is an extension of a generic pipeline developed for pig at ABGC. Modifications include use of bwa 5.9 in stead of later versions, as per the requirements of the 1000 Bulls consortium. Furthermore, the script will automate setting metadata in the BAM files such as including the official 1000 Bulls Ids of the individual cows. Another modification is the use of an sqlite-based database that holds some the metadata required for performing the mapping and setting the correct ids. The sqlite database should be called 'cow_schema.db' and should be in the same working directory as the Python3 master script. Currently, the following two tables should be present in the database: <code>cow_schema_main</code> and <code>bulls1K_id</code>. | ||
Code to create the <code>cow_schema_main</code> table: | |||
<source lang='sql'> | <source lang='sql'> | ||
| Line 5: | Line 7: | ||
</source> | </source> | ||
The table will hold one line for each fastq file (seq_file_name). Archive refers to the base directory that holds the fastq file. Animal ID can be a trivial name. Note that the pipeline will assume files to be gzipped plain-text fastq. The md5sums are from the gzipped files. | |||
Table: cow_schema_main: | Table: cow_schema_main: | ||
archive seq_file_name animal_id md5sum_zipped | archive seq_file_name animal_id md5sum_zipped | ||
| Line 10: | Line 13: | ||
SZAIPI019130-16 121202_I598_FCD1JRLACXX_L6_SZAIPI019130-16_2.fq.gz.clean.dup.clean.gz BOV-WUR-1 333aee62675082d42c2355cc7f21e89b | SZAIPI019130-16 121202_I598_FCD1JRLACXX_L6_SZAIPI019130-16_2.fq.gz.clean.dup.clean.gz BOV-WUR-1 333aee62675082d42c2355cc7f21e89b | ||
Code to create the <code>bulls1K_id</code> table: | |||
<source lang='sql'> | <source lang='sql'> | ||
CREATE TABLE bulls1K_id (animal_id text not null, bull1K_id text not null primary key, tmp_inserted datetime default current_timestamp); | CREATE TABLE bulls1K_id (animal_id text not null, bull1K_id text not null primary key, tmp_inserted datetime default current_timestamp); | ||
Revision as of 22:32, 26 November 2013
The 1000 Bulls mapping pipeline as currently implemented at ABGC is an extension of a generic pipeline developed for pig at ABGC. Modifications include use of bwa 5.9 in stead of later versions, as per the requirements of the 1000 Bulls consortium. Furthermore, the script will automate setting metadata in the BAM files such as including the official 1000 Bulls Ids of the individual cows. Another modification is the use of an sqlite-based database that holds some the metadata required for performing the mapping and setting the correct ids. The sqlite database should be called 'cow_schema.db' and should be in the same working directory as the Python3 master script. Currently, the following two tables should be present in the database: cow_schema_main and bulls1K_id.
Code to create the cow_schema_main table:
<source lang='sql'> CREATE TABLE cow_schema_main (archive text not null, seq_file_name text not null primary key, animal_id text not null, md5sum_zipped text not null, tmp_inserte datetime default current_timestamp); </source>
The table will hold one line for each fastq file (seq_file_name). Archive refers to the base directory that holds the fastq file. Animal ID can be a trivial name. Note that the pipeline will assume files to be gzipped plain-text fastq. The md5sums are from the gzipped files.
Table: cow_schema_main: archive seq_file_name animal_id md5sum_zipped SZAIPI019130-16 121202_I598_FCD1JRLACXX_L6_SZAIPI019130-16_1.fq.gz.clean.dup.clean.gz BOV-WUR-1 f95b35158fe5802a6d6ca18a0e973e87 SZAIPI019130-16 121202_I598_FCD1JRLACXX_L6_SZAIPI019130-16_2.fq.gz.clean.dup.clean.gz BOV-WUR-1 333aee62675082d42c2355cc7f21e89b
Code to create the bulls1K_id table:
<source lang='sql'>
CREATE TABLE bulls1K_id (animal_id text not null, bull1K_id text not null primary key, tmp_inserted datetime default current_timestamp);
</source>
TABLE bulls1K_id animal_id bull1K_id BOV-WUR-1 HOLNLDM000120873995 BOV-WUR-2 HOLNLDM000811488961
<source lang='bash'>
for i in `seq 1 15`; do echo $i; python3 ABGC_mapping_v2.py -i BOV-WUR-$i -a /srv/mds01/shared/Bulls1000/F12FPCEUHK0755_alq121122/cleandata/ -r /srv/mds01/shared/Bulls1000/UMD31/umd_3_1.fa -t 12 -s cow -m bwa-aln -b 5.9 -o; done </source>
Calculating coverage stats:
<source lang='bash'> java -Xmx4G -jar GenomeAnalysisTK.jar -T DepthOfCoverage -R umd_3_1.fa -I BOV-WUR-2_rh.dedup_st.reA.bam --omitDepthOutputAtEachBase --logging_level ERROR --summaryCoverageThreshold 10 --summaryCoverageThreshold 20 --summaryCoverageThreshold 30 --summaryCoverageThreshold 40 --summaryCoverageThreshold 50 --summaryCoverageThreshold 80 --summaryCoverageThreshold 90 --summaryCoverageThreshold 100 --summaryCoverageThreshold 150 --minBaseQuality 15 --minMappingQuality 30 --start 1 --stop 1000 --nBins 999 -dt NONE -o BOV-WUR-2_rh.dedup_st.reA.coverage </source>