Team III Genome Assembly Group: Difference between revisions
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== Introduction/Background == | == Introduction/Background == | ||
[[File:3_assembly_pipeline.png|border|center| | [[File:3_assembly_pipeline.png|border|center|700px]] | ||
:::::::::::::::::::::'''Figure 1:''' Proposed assembly pipeline | :::::::::::::::::::::'''Figure 1:''' Proposed assembly pipeline. | ||
=== Lectures === | === Lectures === | ||
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Afterwards, we compared fastp's trimming features with those of Trimmomatic (http://www.usadellab.org/cms/?page=trimmomatic). Our data did not contain adapters, so we did not need an additional tool like CutAdapt (https://cutadapt.readthedocs.io/en/stable/) to remove them. We showed that most, if not all, of Trimmomatic's trimming features can be replicated in fastp. Furthermore, fastp contains a feature specifically for paired-end data where it can use a high-read to correct low confidence bases in its mate. Fastp has the added advantage of combining both quality control and trimming into a single step, increasing the speed and usability of our pipeline. Since we had 50 input files in our pipeline, we used MultiQC to consolidate the 50 separate quality control reports generated by fastp into a single report. | Afterwards, we compared fastp's trimming features with those of Trimmomatic (http://www.usadellab.org/cms/?page=trimmomatic). Our data did not contain adapters, so we did not need an additional tool like CutAdapt (https://cutadapt.readthedocs.io/en/stable/) to remove them. We showed that most, if not all, of Trimmomatic's trimming features can be replicated in fastp. Furthermore, fastp contains a feature specifically for paired-end data where it can use a high-read to correct low confidence bases in its mate. Fastp has the added advantage of combining both quality control and trimming into a single step, increasing the speed and usability of our pipeline. Since we had 50 input files in our pipeline, we used MultiQC to consolidate the 50 separate quality control reports generated by fastp into a single report. | ||
=== Pre-Trim Quality Control === | |||
[[File:3_QC_pretrim.jpg|border|center|600px]] | |||
:::::::::::::::::::::'''Figure 2:''' Pre-trim quality control curves of our 50 samples. Plot generated using fastp and MultiQC. | |||
Read 1 had high quality reads, so each of the 50 read 1 samples passed the mean quality score threshold of 28. However, read 2 was of lower quality, with only 25 samples passing the same mean quality threshold. This was mostly due to low quality regions at the 5' end of read 2. Because of this notable difference in quality between the two reads, we decided to trim read 1 and read 2 using different parameters. According to (https://www.nature.com/articles/s41598-019-39076-7), trimming does not have a clear effect on assembly quality but speeds up most assembly implementations. In order to obtain high quality data to pass to the assemblers, we wanted to trim the low-quality regions at the 3' and 5' ends of each read while | |||
retaining the high-quality middle regions. | |||
=== Trimming Parameters === | === Trimming Parameters === | ||
The following arguments were supplied to fastp in order to trim our data: -f 5 -F 30 -t 10 -e 28 -c -5 3 -M 27. | |||
* -f 5 - globally trim 5 bases from front of mate 1 | * -f 5 - globally trim 5 bases from front of mate 1 | ||
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* -t 10 - trim 10 bases from end of both mates | * -t 10 - trim 10 bases from end of both mates | ||
* -e 28 - discard reads with an average quality score under 28 | * -e 28 - discard reads with an average quality score under 28 | ||
* -c - turns on paired-end base correction | * -c - turns on paired-end base correction, which slightly increased the quality of the 3' end of mate 2 | ||
* -5 3 - turns on sliding window trimming from 5' end, only bases within the window that don't meet the threshold are discarded, window size of 3 | * -5 3 - turns on sliding window trimming from 5' end, only bases within the window that don't meet the threshold are discarded, window size of 3 | ||
* -M 27 - quality threshold of 27 for sliding window | * -M 27 - quality threshold of 27 for sliding window | ||
| Line 43: | Line 43: | ||
=== Post-Trimming Quality Control === | === Post-Trimming Quality Control === | ||
[[File:3_QC_posttrim.jpg| | |||
[[File:3_filtered_reads.png| | [[File:3_QC_posttrim.jpg|border|center|600px]] | ||
:::::::::::::::::::::'''Figure 3''': Post-trim quality control curves of our 50 samples, generated using fastp and MultiQC. | |||
[[File:3_filtered_reads.png|border|center|600px]] | |||
:::::::::::::::::::::'''Figure 4''': Percentage of reads in each sample that passed the filters, generated using fastp and MultiQC. | |||
provide some summary statistics saying that we didn't trim too much | provide some summary statistics saying that we didn't trim too much | ||
Revision as of 22:43, 22 February 2020
Introduction/Background
- Figure 1: Proposed assembly pipeline.
Lectures
Quality Control/Trimming
Tools used:
- fastp (https://github.com/OpenGene/fastp)
- MultiQC (https://multiqc.info/)
Selection of Tools
For quality control, we compared two tools, fastp and FastQC (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). We first proved that the two programs generated identical information when run on identical fastq files, after which we compared the information displayed in the reports for both. While FastQC creates highly informative per-base sequence quality graphs, it runs significantly slower than fastp. We were able to extract biologically significant information from the charts generated from fastp, so we decided to use fastp for quality control.
Afterwards, we compared fastp's trimming features with those of Trimmomatic (http://www.usadellab.org/cms/?page=trimmomatic). Our data did not contain adapters, so we did not need an additional tool like CutAdapt (https://cutadapt.readthedocs.io/en/stable/) to remove them. We showed that most, if not all, of Trimmomatic's trimming features can be replicated in fastp. Furthermore, fastp contains a feature specifically for paired-end data where it can use a high-read to correct low confidence bases in its mate. Fastp has the added advantage of combining both quality control and trimming into a single step, increasing the speed and usability of our pipeline. Since we had 50 input files in our pipeline, we used MultiQC to consolidate the 50 separate quality control reports generated by fastp into a single report.
Pre-Trim Quality Control
- Figure 2: Pre-trim quality control curves of our 50 samples. Plot generated using fastp and MultiQC.
Read 1 had high quality reads, so each of the 50 read 1 samples passed the mean quality score threshold of 28. However, read 2 was of lower quality, with only 25 samples passing the same mean quality threshold. This was mostly due to low quality regions at the 5' end of read 2. Because of this notable difference in quality between the two reads, we decided to trim read 1 and read 2 using different parameters. According to (https://www.nature.com/articles/s41598-019-39076-7), trimming does not have a clear effect on assembly quality but speeds up most assembly implementations. In order to obtain high quality data to pass to the assemblers, we wanted to trim the low-quality regions at the 3' and 5' ends of each read while retaining the high-quality middle regions.
Trimming Parameters
The following arguments were supplied to fastp in order to trim our data: -f 5 -F 30 -t 10 -e 28 -c -5 3 -M 27.
- -f 5 - globally trim 5 bases from front of mate 1
- -F 30 - globally trim 30 bases from front of mate 2
- -t 10 - trim 10 bases from end of both mates
- -e 28 - discard reads with an average quality score under 28
- -c - turns on paired-end base correction, which slightly increased the quality of the 3' end of mate 2
- -5 3 - turns on sliding window trimming from 5' end, only bases within the window that don't meet the threshold are discarded, window size of 3
- -M 27 - quality threshold of 27 for sliding window
Post-Trimming Quality Control
- Figure 3: Post-trim quality control curves of our 50 samples, generated using fastp and MultiQC.
- Figure 4: Percentage of reads in each sample that passed the filters, generated using fastp and MultiQC.
provide some summary statistics saying that we didn't trim too much