International Standards for Genomes, Transcriptomes, and Metagenomes

Abstract

Challenges and biases in preparing, characterizing, and sequencing DNA and RNA can have significant impacts on research in genomics across all kingdoms of life, including experiments in single-cells, RNA profiling, and metagenomics (across multiple genomes). Technical artifacts and contamination can arise at each point of sample manipulation, extraction, sequencing, and analysis. Thus, the measurement and benchmarking of these potential sources of error are of paramount importance as next-generation sequencing (NGS) projects become more global and ubiquitous. Fortunately, a variety of methods, standards, and technologies have recently emerged that improve measurements in genomics and sequencing, from the initial input material to the computational pipelines that process and annotate the data. Here we review current standards and their applications in genomics, including whole genomes, transcriptomes, mixed genomic samples (metagenomes), and the modified bases within each (epigenomes and epitranscriptomes). These standards, tools, and metrics are critical for quantifying the accuracy of NGS methods, which will be essential for robust approaches in clinical genomics and precision medicine.

Comprehensive Benchmarking and Ensemble Approaches for Metagenomic Classifiers

“…Using a deeply sequenced, complex environmental sample from the New York City subway system (100 M reads from sample P00497), we subsampled the full dataset to identify the depth (5,10,15,20,30,40,50, and 75 M reads) at which each tool recovered its maximum number of predicted species (Fig. 6b).…”

“…$ makeblastdb -in NCBI_DB/BLAST_DB.fasta -out NCBI_DB/BLAST_DB.fasta -dbtype nucl Classification of each sample (<sample > .fasta) then proceeded through the following steps: 1) BLAST alignment $ blastn -query < sampleID > .fasta -out < sampleID > .blast -outfmt 6 -db NCBI_DB/ BLAST_DB.fasta -num_threads 10 We converted the sample file into FASTA file if the sample file was in FASTQ format and used the default settings to align the reads with BLAST. 2) LGF file construction $ python BLAST_TO_LGF.py < sampleID > .blast NCBI_DB/NCBI_Ref_Genome.txt < avg_length > <seq_type > The graph-based representation from the BLAST alignments is built into a LGF (Lemon Graph Format) file.…”

Ultra-deep, long-read nanopore sequencing of mock microbial community standards

“…Mock community standards are useful for the development of genomics methods [14], and for the validation of existing laboratory, software and bioinformatics approaches. For example, validating the accuracy of a taxonomic identi cation pipeline is important, because the consequences of erroneous taxonomic identi cation from a metagenomic analysis may be severe, e.g.…”

“…in public health microbiology (such as in the well-publicised case of anthrax and plague in the New York Subway [15]) or missed diagnoses in the clinic. Mock community standards can also be used as positive controls during laboratory work, for example to validate that DNA extraction methods will yield expected representation of a sampled community [14].…”

Implications of error-prone long-read whole-genome shotgun sequencing on characterizing reference microbiomes

“…While the ONT have great potential, complete and contiguous de novo metagenome assembly is still constrained by the high error rate (∼15%) of single-molecule long-read sequence data[34]. Therefore, a comprehensive evaluation of long-read bioinformatics tools in microbial profiling is needed[35]. Nicholls et al [36] presented Nanopore sequencing data sets of two mock communities with 10 microbial species from ZymoBIOMICS[37].…”