Team III Genome Assembly Group

Group Members: Deepali Kundnani, Aparna Maddala, Swetha Singu, Yiqiong Xiao, Ruize Yang

Lectures

• Team 3 Genome Assembly: Background and Strategy
• Team 3 Genome Assembly: Results

Introduction

Genome assembly is the process of combining the reads generated by sequencing technology into contiguous sequences that ultimately span a large portion of a genome. This can either be accomplished by examining how the reads overlap or examining their similarity to a reference genome. A standard genome assembly pipeline includes quality control, trimming, post-trimming quality control, sequence assembly, and assembly validation [1]. The objective of this project was to assemble bacterial genomes from 50 fastq files provided by CDC PulseNET. All of the samples were generated by paired-end sequencing on the Illumina platform.

Methods

Pipeline for Testing

Figure 1: Proposed assembly pipeline. fastp and MultiQC are used for quality control/trimming, sequence assembling is done in SPAdes, and assembly validation is conducted in Quast and BUSCO. The yellow bubbles represent the output files generated at each step.

Quality Control/Trimming

Tools used:

• fastp (https://github.com/OpenGene/fastp)
• MultiQC (https://multiqc.info/)

Tool Selection

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. A) Read 1 quality curves; B) Read 2 quality curves.

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 a comparison of trimming parameters on the assembly quality of Cyanoderma ruficeps genomes, trimming does not have a clear effect on assembly quality but speeds up most assembly implementations [2]. Therefore, in order to obtain high quality data to pass to the assemblers, we decided to completely 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 trims 5 bases from 5' end of mate 1
• -F 30 - globally trims 30 bases from 5' end of mate 2
• -t 10 - globally trims 10 bases from 3' end of both mates
• -e 28 - discards 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 with a window size of 3
• -M 27 - sets a quality threshold of 27 for sliding window

Assembly

Parameters considered when selecting assembly tool

[namedrop the two comparative studies we referenced here]

Tools considered

MaSuRCA

Version: 3.3.5

MaSuRCA, or the Maryland Super-Read Celera Assembler, uses a combination of the OLC and de Bruijn Graph algorithms to assemble data. MaSuRCA normally creates longer contigs and offers higher N50 values. It works best on untrimmed data, which is possibly responsible for its slower computational time [2].

Results

Post-Trimming Quality Control

Figure 3: Post-trim quality control curves of our 50 samples. Plot generated using fastp and MultiQC. A) Read 1 quality curves; B) Read 2 quality curves.

After trimming, the quality of our read 1 data improved slightly. However, the largest difference can be observed in read 2 data, with all 50 of the samples passing the mean quality threshold of a Phred score of 28. The 5' and 3' ends of some of the read 2 samples are of low quality, but more aggressive trimming would most probably also eliminate high-confidence regions.

Figure 4: Percentage of reads in each sample that passed the filters, generated using fastp and MultiQC.

A vast majority of reads in each sample passed our filter, with at most 11% of reads falling below our quality and length thresholds and at most []% of bases trimmed from each sample.

Inter-Assembler Assessments

For quality assessment of Genome Assembly we make use of the widely used software

QUAST(QUaslity ASsessment Tool for Genome Assembly)

with

BUSCO(Benchmarking Universal Single Copy Orthologs)

to complement the analysis. With Quast, we focus mainly on N50, L50, number of Contigs, N's per 100kb. BUSCO provides us with single copy, duplicated, missing and fragmented Benchmarking orthologs. Assemblies with poor error correction lead to higher duplicated BUSCOs and ones with data loss lead to missing or fragmented BUSCOs. Lower completeness scores with many fragmented BUSCOs can also be suggestive of missassemblies that Quast might not be able to detect.

We first compared a subset of 10 samples selected based on trimming quality. The tools selected were 1.SPAdes 2.SKESA 3. Abyss with KmerGenie 4. Masurca.

We rule out Abyss based on presence of N's per 100Kbp. In the previous analysis of different kmers on Abyss, the genome assemblies showed N's if the the kmers were too low or too high. Its possible that the kmers suggested by KmerGenie are lower than optimal.

In this analysis, it is also evident that MaSuRCA has lower Completeness score and missing BUSCOs suggesting loss of valuable information from the Assembly. This again can be due high kmer selection which was seen to be around 99 for most of the samples. MaSuRCA can be a good tool for reads with higher sequencing depth or coverage, organisms cultured prior to sequencing. Its safe to assume the worst case scenario of working with non cultured organisms having lower sequencing depth, we rule out MaSuRCA from the list of Assemblers for final Validation.

Since the comparisons are inconclusive between Spades and SKESA by just using 10 libraries, we assemble genomes from all 50 libraries provided to us. As seen from metrics from QUAST and BUSCO, Spades clearly out performs SKESA and its because there are some samples that are handled better in SPades than SKESA.

References

1. Dominguez Del Angel V, Hjerde E, Sterck L et al. Ten steps to get started in Genome Assembly and Annotation [version 1; peer review: 2 approved]. F1000Research 2018, 7(ELIXIR):148 (https://doi.org/10.12688/f1000research.13598.1)
2. Yang, S.-F.; Lu, C.-W.; Yao, C.-T.; Hung, C.-M. To Trim or Not to Trim: Effects of Read Trimming on the De Novo Genome Assembly of a Widespread East Asian Passerine, the Rufous-Capped Babbler (Cyanoderma ruficeps Blyth). Genes 2019, 10, 737.