Identification and quantification of chimeric sequencing reads in a highly multiplexed RAD-seq protocol

Highly multiplexed approaches have become a common practice in genomic studies. They have improved the cost-effectiveness of genotyping hundreds of individuals by using combinatorially-barcoded adapters. These strategies, however, can potentially misassign reads to incorrect samples. Here we used a modified quaddRAD protocol to analyse the occurrence of index hopping and PCR chimeras in a series of experiments with up to a 100 multiplexed samples per sequencing lane (total n = 639). We created two types of sequencing libraries: four libraries of Type A, where PCR reactions were run on individual samples before multiplexing, and three libraries of Type B, where PCRs were run on pooled samples. We used fixed pairs of inner barcodes to identify chimeric reads. Type B libraries show a higher percentage of misassigned reads (1.15%) compared to Type A libraries (0.65%). We also quantify the commonly undetectable chimeric sequences that occur whenever multiplexed groups of samples with different outer barcodes are sequenced together on a single flow cell. Our results suggest that these types of chimeric sequences represent up to 1.56% and 1.29% of reads in Type A and B libraries, respectively. We review the source of such errors, provide recommendations for developing highly-multiplexed RAD-seq protocols and analysing the resulting data to minimise the generation of chimeric sequences, allow their quantification, and provide finer control over the number of PCR cycles necessary to generate enough input DNA for library preparation.

Monitoring SARS-CoV-2 Populations in Wastewater by Amplicon Sequencing and Using the Novel Program SAM Refiner

Sequencing SARS-CoV-2 from wastewater has become a useful tool in monitoring the spread of variants. We use a novel computation workflow with SARS-CoV-2 amplicon sequencing in order to track wastewater populations of the virus. As part of this workflow, we developed a program for both variant reporting and removal of PCR generated chimeric sequences. With these methods, we are able to track viral population dynamics over time. We observe the emergence of the variants of concern B.1.1.7 and P.1, and their displacement of the D614G B.1 variant.

The Bellerophon pipeline, improving de novo transcriptomes and removing chimeras

Transcriptome quality control is an important step in RNA-seq experiments. However, the quality of de novo assembled transcriptomes is difficult to assess, due to the lack of reference genome to compare the assembly to. We developed a method to assess and improve the quality of de novo assembled transcriptomes by focusing on the removal of chimeric sequences. These chimeric sequences can be the result of faulty assembled contigs, merging two transcripts into one. The developed method is incorporated into a pipeline, that we named Bellerophon, which is broadly applicable and easy to use. Bellerophon first uses the quality-assessment tool TransRate to indicate the quality, after which it uses a Transcripts Per Million (TPM) filter to remove lowly expressed contigs and CD-HIT-EST to remove highly identical contigs. To validate the quality of this method, we performed three benchmark experiments: 1) a computational creation of chimeras, 2) identification of chimeric contigs in a transcriptome assembly, 3) a simulated RNAseq experiment using a known reference transcriptome. Overall, the Bellerophon pipeline was able to remove between 40 to 91.9% of the chimeras in transcriptome assemblies and removed more chimeric than non-chimeric contigs. Thus, the Bellerophon sequence of filtration steps is a broadly applicable solution to improve transcriptome assemblies.