August 19, 2021  |  

Case Study: Sequencing an historic bacterial collection for the future

The UK’s National Collection of Type Cultures (NCTC) is a unique collection of more than 5,000 expertly preserved and authenticated bacterial cultures, many of historical significance. Founded in 1920, NCTC is the longest established collection of its type anywhere in the world, with a history of its own that has reflected — and contributed to — the evolution of microbiology for more than 100 years.


June 1, 2021  |  

Harnessing kinetic information in Single-Molecule, Real-Time Sequencing.

Single-Molecule Real-Time (SMRT) DNA sequencing is unique in that nucleotide incorporation events are monitored in real time, leading to a wealth of kinetic information in addition to the extraction of the primary DNA sequence. The dynamics of the DNA polymerase that is observed adds an additional dimension of sequence-dependent information, and can be used to learn more about the molecule under study. First, the primary sequence itself can be determined more accurately. The kinetic data can be used to corroborate or overturn consensus calls and even enable calling bases in problematic sequence contexts. Second, using the kinetic information, we can detect and discriminate numerous chemical base modifications as a by-product of ordinary sequencing. Examples of applying these capabilities include (i) the characterization of the epigenome of microorganisms by directly sequencing the three common prokaryotic epigenetic base modifications of 4-methylcytosine, 5- methylcytosine and 6-methyladenine; (ii) the characterization of known and novel methyltransferase activities; (iii) the direct sequencing and differentiation of the four eukaryotic epigenetic forms of cytosine (5-methyl, 5-hydroxymethyl, 5-formyl, and 5-carboxylcytosine) with first applications to map them with single base-pair and DNA strand resolution across mammalian genomes; (iv) the direct sequencing and identification of numerous modified DNA bases arising from DNA damage; and (v) an exploration of the mitochondrial genome for known and novel base modifications. We will show our progress towards a generic, open-source algorithm for exploiting kinetic information for any of these purposes.


June 1, 2021  |  

Complete HIV-1 genomes from single molecules: Diversity estimates in two linked transmission pairs using clustering and mutual information.

We sequenced complete HIV-1 genomes from single molecules using Single Molecule, Real- Time (SMRT) Sequencing and derive de novo full-length genome sequences. SMRT sequencing yields long-read sequencing results from individual DNA molecules with a rapid time-to-result. These attributes make it a useful tool for continuous monitoring of viral populations. The single-molecule nature of the sequencing method allows us to estimate variant subspecies and relative abundances by counting methods. We detail mathematical techniques used in viral variant subspecies identification including clustering distance metrics and mutual information. Sequencing was performed in order to better understand the relationships between the specific sequences of transmitted viruses in linked transmission pairs. Samples representing HIV transmission pairs were selected from the Zambia Emory HIV Research Project (Lusaka, Zambia) and sequenced. We examine Single Genome Amplification (SGA) prepped samples and samples containing complex mixtures of genomes. Whole genome consensus estimates for each of the samples were made. Genome reads were clustered using a simple distance metric on aligned reads. Appropriate thresholds were chosen to yield distinct clusters of HIV genomes within samples. Mutual information between columns in the genome alignments was used to measure dependence. In silico mixtures of reads from the SGA samples were made to simulate samples containing exactly controlled complex mixtures of genomes and our clustering methods were applied to these complex mixtures. SMRT Sequencing data contained multiple full-length (greater than 9 kb) continuous reads for each sample. Simple whole genome consensus estimates easily identified transmission pairs. The clustering of the genome reads showed diversity differences between the samples, allowing us to characterize the diversity of the individual quasi-species comprising the patient viral populations across the full genome. Mutual information identified possible dependencies of different positions across the full HIV-1 genome. The SGA consensus genomes agreed with prior Sanger sequencing. Our clustering methods correctly segregated reads to their correct originating genome for the synthetic SGA mixtures. The results open up the potential for reference-agnostic and cost effective full genome sequencing of HIV-1.


June 1, 2021  |  

Advances in sequence consensus and clustering algorithms for effective de novo assembly and haplotyping applications.

One of the major applications of DNA sequencing technology is to bring together information that is distant in sequence space so that understanding genome structure and function becomes easier on a large scale. The Single Molecule Real Time (SMRT) Sequencing platform provides direct sequencing data that can span several thousand bases to tens of thousands of bases in a high-throughput fashion. In contrast to solving genomic puzzles by patching together smaller piece of information, long sequence reads can decrease potential computation complexity by reducing combinatorial factors significantly. We demonstrate algorithmic approaches to construct accurate consensus when the differences between reads are dominated by insertions and deletions. High-performance implementations of such algorithms allow more efficient de novo assembly with a pre-assembly step that generates highly accurate, consensus-based reads which can be used as input for existing genome assemblers. In contrast to recent hybrid assembly approach, only a single ~10 kb or longer SMRTbell library is necessary for the hierarchical genome assembly process (HGAP). Meanwhile, with a sensitive read-clustering algorithm with the consensus algorithms, one is able to discern haplotypes that differ by less than 1% different from each other over a large region. One of the related applications is to generate accurate haplotype sequences for HLA loci. Long sequence reads that can cover the whole 3 kb to 4 kb diploid genomic regions will simplify the haplotyping process. These algorithms can also be applied to resolve individual populations within mixed pools of DNA molecules that are similar to each, e.g., by sequencing viral quasi-species samples.


June 1, 2021  |  

Automated, non-hybrid de novo genome assemblies and epigenomes of bacterial pathogens.

Understanding the genetic basis of infectious diseases is critical to enacting effective treatments, and several large-scale sequencing initiatives are underway to collect this information. Sequencing bacterial samples is typically performed by mapping sequence reads against genomes of known reference strains. While such resequencing informs on the spectrum of single-nucleotide differences relative to the chosen reference, it can miss numerous other forms of variation known to influence pathogenicity: structural variations (duplications, inversions), acquisition of mobile elements (phages, plasmids), homonucleotide length variation causing phase variation, and epigenetic marks (methylation, phosphorothioation) that influence gene expression to switch bacteria from non- pathogenic to pathogenic states. Therefore, sequencing methods which provide complete, de novo genome assemblies and epigenomes are necessary to fully characterize infectious disease agents in an unbiased, hypothesis-free manner. Hybrid assembly methods have been described that combine long sequence reads from SMRT DNA Sequencing with short reads (SMRT CCS (circular consensus) or second-generation reads), wherein the short reads are used to error-correct the long reads which are then used for assembly. We have developed a new paradigm for microbial de novo assemblies in which SMRT sequencing reads from a single long insert library are used exclusively to close the genome through a hierarchical genome assembly process, thereby obviating the need for a second sample preparation, sequencing run, and data set. We have applied this method to achieve closed de novo genomes with accuracies exceeding QV50 (>99.999%) for numerous disease outbreak samples, including E. coli, Salmonella, Campylobacter, Listeria, Neisseria, and H. pylori. The kinetic information from the same SMRT Sequencing reads is utilized to determine epigenomes. Approximately 70% of all methyltransferase specificities we have determined to date represent previously unknown bacterial epigenetic signatures. With relatively short sequencing run times and automated analysis pipelines, it is possible to go from an unknown DNA sample to its complete de novo genome and epigenome in about a day.


June 1, 2021  |  

Single Molecule, Real-Time Sequencing for base modification detection in eukaryotic organisms: Coprinopsis cinerea.

Single Molecule Real-Time (SMRT) DNA sequencing provides a wealth of kinetic information beyond the extraction of the primary DNA sequence, and this kinetic information can provide for the direct detection of modified bases present in genomic DNA. This method has been demonstrated for base modification detection in prokaryotes at base and strand resolutions. In eukaryotes, the common base modifications known to exist are the cytosine variants including methyl, hydroxymethyl, formyl and carboxyl forms. Each of these modifications exhibits different signatures in SMRT kinetic data, allowing for unprecedented possibilities to differentiate between them in direct sequencing data. We present early results of directly sequencing different base modifications in eukaryotic genomic DNA using this method.


June 1, 2021  |  

Automated, non-hybrid de novo genome assemblies and epigenomes of bacterial pathogens

Understanding the genetic basis of infectious diseases is critical to enacting effective treatments, and several large-scale sequencing initiatives are underway to collect this information. Sequencing bacterial samples is typically performed by mapping sequence reads against genomes of known reference strains. While such resequencing informs on the spectrum of single nucleotide differences relative to the chosen reference, it can miss numerous other forms of variation known to influence pathogenicity: structural variations (duplications, inversions), acquisition of mobile elements (phages, plasmids), homonucleotide length variation causing phase variation, and epigenetic marks (methylation, phosphorothioation) that influence gene expression to switch bacteria from non-pathogenic to pathogenic states. Therefore, sequencing methods which provide complete, de novo genome assemblies and epigenomes are necessary to fully characterize infectious disease agents in an unbiased, hypothesis-free manner. Hybrid assembly methods have been described that combine long sequence reads from SMRT DNA sequencing with short, high-accuracy reads (SMRT (circular consensus sequencing) CCS or second-generation reads) to generate long, highly accurate reads that are then used for assembly. We have developed a new paradigm for microbial de novo assemblies in which long SMRT sequencing reads (average readlengths >5,000 bases) are used exclusively to close the genome through a hierarchical genome assembly process, thereby obviating the need for a second sample preparation, sequencing run and data set. We have applied this method to achieve closed de novo genomes with accuracies exceeding QV50 (>99.999%) to numerous disease outbreak samples, including E. coli, Salmonella, Campylobacter, Listeria, Neisseria, and H. pylori. The kinetic information from the same SMRT sequencing reads is utilized to determine epigenomes. Approximately 70% of all methyltransferase specificities we have determined to date represent previously unknown bacterial epigenetic signatures. The process has been automated and requires less than 1 day from an unknown DNA sample to its complete de novo genome and epigenome.


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