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This article mainly explains "how to use Trimmomatic". The content in the article is simple and clear, and it is easy to learn and understand. Please follow the editor's train of thought to study and learn how to use Trimmomatic.

Accusations of second-generation sequencing data generally include the following steps:

Remove bases whose tail-end base mass is less than the specified value (usually 20). You can simply prune a single base, that is, delete it from the end until the read base mass is higher than 20, or you can perform a sliding window pruning, that is, starting from the end with a sliding window with a specified number of bases, until the average base mass in the sliding window is higher than 20.

Remove reads whose end-trimmed length is less than the specified value. The specified values vary with different projects. In general, reads smaller than 50bp is removed from the macro genome (50bp is not enough to generate k-mer), while amplifiers are sequenced based on the length of raw reads and the length of PCR inserts. For example, if the V4 region is about 260bp, then you can remove the sum of double-terminal reads that is less than 280bp (otherwise it is not enough to splice).

Other requirements, such as the removal of reads containing too much N (that is, unable to read sites), the removal of fully duplicated reads, and so on.

Usually quality control requires us to write our own scripts to complete. Trimmomatic is a convenient and easy-to-use Illumina sequencing data quality control tool, which can save us a lot of code tasks. It has been cited more than 10,000 times since its publication. Installation can use conda:conda install-c trimmomaticTrimmomatic basic usage and default parameters are as follows: java-jar trimmomatic-0.30.jar PE-threads 20-phred33 R1.fq R2.fq clean.R1.fq unpaired.R1.fq clean.R2.fq unpaired.R2.fq ILLUMINACLIP:TruSeq3-PE.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36 parameters are explained as follows: PE/SE setting to process the reads of Paired-End or Single-End The input and output parameters are slightly different. -threads sets the number of multithreaded runs, that is, the number of kernels-phred33 sets the quality format of the base. You can choose pred64ILLUMINACLIP:TruSeq3-PE.fa:2:30:10 to remove the adapter sequence. The parameters are followed by the fasta file of the adapter sequence: the maximum number of mismatch allowed: the matching base threshold in palindrome mode and the matching base threshold in simple mode. The base TRAILING:3 with the first base mass less than 3 was removed from the LEADING:3, and the base SLIDINGWINDOW:4:15 with the base mass less than 3 at the tail end was pruned. The size of a Windows was 4 bases, and the average base mass was less than 15. MINLEN:50 minimum reads length CROP: keep the reads to the specified length HEADCROP: remove the specified length TOPHRED33 at the first end of the reads to convert the base mass to the pred33 format TOPHRED64 to convert the base mass to the pred64 format below to introduce the use of the software through some examples:

Remove the bases whose tail base mass is less than 20 (that is, delete them from the end until the read base mass is higher than 20) and remove the small sequence fragments whose length is less than 150 after cutting: java-jar trimmomatic-0.30.jar PE-threads 20-phred33 R1.fq R2.fq clean.R1.fq unpaired.R1.fq clean.R2.fq unpaired.R2.fq TRAILING:20 MINLEN:150 is trimmed using the terminal slide window At the same time, remove the small fragments whose length is too short (less than 50bp) after quality control, as follows: after java-jar trimmomatic-0.33.jar PE-threads 20-phred33 rm_dup_N_trim_1.fq rm_dup_N_trim_2.fq clean_1.fq unp_clean_1.fq clean_2.fq unp_clean_2.fq SLIDINGWINDOW:4:20 MINLEN:50 quality control, we get clean reads from raw reads. You can also use FastQC for quality visualization again to view the quality control effect:

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