Roger S. Lasken

Bio: Roger S. Lasken is an academic researcher from J. Craig Venter Institute. The author has contributed to research in topics: Multiple displacement amplification & genomic DNA. The author has an hindex of 49, co-authored 82 publications receiving 11489 citations.

Comprehensive human genome amplification using multiple displacement amplification

Abstract: Fundamental to most genetic analysis is availability of genomic DNA of adequate quality and quantity. Because DNA yield from human samples is frequently limiting, much effort has been invested in developing methods for whole genome amplification (WGA) by random or degenerate oligonucleotide-primed PCR. However, existing WGA methods like degenerate oligonucleotide-primed PCR suffer from incomplete coverage and inadequate average DNA size. We describe a method, termed multiple displacement amplification (MDA), which provides a highly uniform representation across the genome. Amplification bias among eight chromosomal loci was less than 3-fold in contrast to 4–6 orders of magnitude for PCR-based WGA methods. Average product length was >10 kb. MDA is an isothermal, strand-displacing amplification yielding about 20–30 μg product from as few as 1–10 copies of human genomic DNA. Amplification can be carried out directly from biological samples including crude whole blood and tissue culture cells. MDA-amplified human DNA is useful for several common methods of genetic analysis, including genotyping of single nucleotide polymorphisms, chromosome painting, Southern blotting and restriction fragment length polymorphism analysis, subcloning, and DNA sequencing. MDA-based WGA is a simple and reliable method that could have significant implications for genetic studies, forensics, diagnostics, and long-term sample storage.

Rapid Amplification of Plasmid and Phage DNA Using Phi29 DNA Polymerase and Multiply-Primed Rolling Circle Amplification

Abstract: We describe a simple method of using rolling circle amplification to amplify vector DNA such as M13 or plasmid DNA from single colonies or plaques. Using random primers and phi29 DNA polymerase, circular DNA templates can be amplified 10,000-fold in a few hours. This procedure removes the need for lengthy growth periods and traditional DNA isolation methods. Reaction products can be used directly for DNA sequencing after phosphatase treatment to inactivate unincorporated nucleotides. Amplified products can also be used for in vitro cloning, library construction, and other molecular biology applications.

Assembling Single-Cell Genomes and Mini-Metagenomes From Chimeric MDA Products

Abstract: Recent advances in single-cell genomics provide an alternative to largely gene-centric metagenomics studies, enabling whole-genome sequencing of uncultivated bacteria. However, single-cell assembly projects are challenging due to (i) the highly nonuniform read coverage and (ii) a greatly elevated number of chimeric reads and read pairs. While recently developed single-cell assemblers have addressed the former challenge, methods for assembling highly chimeric reads remain poorly explored. We present algorithms for identifying chimeric edges and resolving complex bulges in de Bruijn graphs, which significantly improve single-cell assemblies. We further describe applications of the single-cell assembler SPAdes to a new approach for capturing and sequencing “microbial dark matter” that forms small pools of randomly selected single cells (called a mini-metagenome) and further sequences all genomes from the mini-metagenome at once. On single-cell bacterial datasets, SPAdes improves on the recently deve...

Assembling genomes and mini-metagenomes from highly chimeric reads

Abstract: Recent advances in single-cell genomics provide an alternative to gene-centric metagenomics studies, enabling whole genome sequencing of uncultivated bacteria. However, single-cell assembly projects are challenging due to (i) the highly non-uniform read coverage, and (ii) a greatly elevated number of chimeric reads and read pairs. While recently developed single-cell assemblers have addressed the former challenge, methods for assembling highly chimeric reads remain poorly explored. We present algorithms for identifying chimeric edges and resolving complex bulges in de Bruijn graphs, which significantly improve single-cell assemblies. We further describe applications of the single-cell assembler SPAdes to a new approach for capturing and sequencing "dark matter of life" that forms small pools of randomly selected single cells (called a mini-metagenome) and further sequences all genomes from the mini-metagenome at once. We demonstrate that SPAdes enables sequencing mini-metagenomes and benchmark it against various assemblers. On single-cell bacterial datasets, SPAdes improves on the recently developed E+V-SC and IDBA-UD assemblers specifically designed for single-cell sequencing. For standard (multicell) datasets, SPAdes also improves on A5, ABySS, CLC, EULER-SR, Ray, SOAPdenovo, and Velvet.

Unbiased Whole-Genome Amplification Directly From Clinical Samples

Abstract: Preparation of genomic DNA from clinical samples is a bottleneck in genotyping and DNA sequencing analysis and is frequently limited by the amount of specimen available. We use Multiple Displacement Amplification (MDA) to amplify the whole genome 10,000-fold directly from small amounts of whole blood, dried blood, buccal cells, cultured cells, and buffy coats specimens, generating large amounts of DNA for genetic testing. Genomic DNA was evenly amplified with complete coverage and consistent representation of all genes. All 47 loci analyzed from 44 individuals were represented in the amplified DNA at between 0.5- and 3.0-fold of the copy number in the starting genomic DNA template. A high-fidelity DNA polymerase ensures accurate representation of the DNA sequence. The amplified DNA was indistinguishable from the original genomic DNA template in 5 SNP and 10 microsatellite DNA assays on three different clinical sample types for 20 individuals. Amplification of genomic DNA directly from cells is highly reproducible, eliminates the need for DNA template purification, and allows genetic testing from small clinical samples. The low amplification bias of MDA represents a dramatic technical improvement in the ability to amplify a whole genome compared with older, PCR-based methods.

SPAdes: A New Genome Assembly Algorithm and Its Applications to Single-Cell Sequencing

Abstract: The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V−SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online (http://bioinf.spbau.ru/spades). It is distributed as open source software.

SPAdes, a new genome assembly algorithm and its applications to single-cell sequencing ( 7th Annual SFAF Meeting, 2012)

Abstract: The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.

Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences

Abstract: Motivation: In 2001 and 2002, we published two papers (Bioinformatics, 17, 282--283, Bioinformatics, 18, 77--82) describing an ultrafast protein sequence clustering program called cd-hit. This program can efficiently cluster a huge protein database with millions of sequences. However, the applications of the underlying algorithm are not limited to only protein sequences clustering, here we present several new programs using the same algorithm including cd-hit-2d, cd-hit-est and cd-hit-est-2d. Cd-hit-2d compares two protein datasets and reports similar matches between them; cd-hit-est clusters a DNA/RNA sequence database and cd-hit-est-2d compares two nucleotide datasets. All these programs can handle huge datasets with millions of sequences and can be hundreds of times faster than methods based on the popular sequence comparison and database search tools, such as BLAST. Availability: http://cd-hit.org Contact: [email protected]

Integrating single-cell transcriptomic data across different conditions, technologies, and species.

Abstract: Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.