Biology subjects

Michael Schatz

Publications and source records attributed to Michael Schatz.

8 recordsLinked to original sources

bioRxiv2016

GenomeScope: Fast reference-free genome profiling from short reads

SummaryGenomeScope is an open-source web tool to rapidly estimate the overall characteristics of a genome, including genome size, heterozygosity rate, and repeat content from unprocessed short reads. These features are essential for studying genome evolution, and help to choose parameters for downstream analysis. We demonstrate its accuracy on 324 simulated and 16 real datasets with a wide range in genome sizes, heterozygosity levels, and error rates.\n\nAvailability and Implementationhttp://genomescope.org, https://github.com/schatzlab/genomescope.git\n\nContactmschatz@jhu.edu.\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

Bioinformatics↗

bioRxiv2016

Third-generation sequencing and the future of genomics

Third-generation long-range DNA sequencing and mapping technologies are creating a renaissance in high-quality genome sequencing. Unlike second-generation sequencing, which produces short reads a few hundred base-pairs long, third-generation single-molecule technologies generate over 10,000 bp reads or map over 100,000 bp molecules. We analyze how increased read lengths can be used to address longstanding problems in de novo genome assembly, structural variation analysis and haplotype phasing.

Bioinformatics↗

bioRxiv2015

The next 20 years of genome research

The last 20 years have been a remarkable era for biology and medicine. One of the most significant achievements has been the sequencing of the first human genomes, which has laid the foundation for profound insights into human genetics, the intricacies of regulation and development, and the forces of evolution. Incredibly, as we look into the future over the next 20 years, we see the very real potential for sequencing more than one billion genomes, bringing with it even deeper insights into human genetics as well as the genetics of millions of other species on the planet. Realizing this great potential, though, will only be achieved through the integration and development of highly scalable computational and quantitative approaches can keep pace with the rapid improvements to biotechnology. In this perspective, we aim to chart out these future technologies, anticipate the major themes of research, and call out the challenges ahead. One of the largest shifts will be in the training used to prepare the class of 2035 for their highly interdisciplinary world.

Bioinformatics↗

bioRxiv2015

Metassembler: Merging and optimizing de novo genome assemblies

Genome assembly projects typically run multiple algorithms in an attempt to find the single best assembly, although those assemblies often have complementary, if untapped, strengths and weaknesses. We present our metassembler algorithm that merges multiple assemblies of a genome into a single superior sequence. We apply it to the four genomes from the Assemblathon competitions and show it consistently and substantially improves the contiguity and quality of each assembly. We also develop guidelines for metassembly by systematically evaluating 120 permutations of merging the top 5 assemblies of the first Assemblathon competition. The software is open-source at http://metassembler.sourceforge.net.

Bioinformatics↗

bioRxiv2015

Oxford Nanopore Sequencing, Hybrid Error Correction, and de novo Assembly of a Eukaryotic Genome

Monitoring the progress of DNA molecules through a membrane pore has been postulated as a method for sequencing DNA for several decades. Recently, a nanopore-based sequencing instrument, the Oxford Nanopore MinION, has become available that we used for sequencing the S. cerevisiae genome. To make use of these data, we developed a novel open-source hybrid error correction algorithm Nanocorr (https://github.com/jgurtowski/nanocorr) specifically for Oxford Nanopore reads, as existing packages were incapable of assembling the long read lengths (5-50kbp) at such high error rate (between [~]5 and 40% error). With this new method we were able to perform a hybrid error correction of the nanopore reads using complementary MiSeq data and produce a de novo assembly that is highly contiguous and accurate: the contig N50 length is more than ten-times greater than an Illumina-only assembly (678kb versus 59.9kbp), and has greater than 99.88% consensus identity when compared to the reference. Furthermore, the assembly with the long nanopore reads presents a much more complete representation of the features of the genome and correctly assembles gene cassettes, rRNAs, transposable elements, and other genomic features that were almost entirely absent in the Illumina-only assembly.\n\nReviewer link to datahttp://schatzlab.cshl.edu/data/nanocorr/

Bioinformatics↗

bioRxiv2014

Interactive analysis and quality assessment of single-cell copy-number variations

We present an open-source visual-analytics web platform, Ginkgo (http://qb.cshl.edu/ginkgo), for the interactive analysis and quality assessment of single-cell copy-number alterations. Ginkgo automatically constructs copy-number profiles of individual cells from mapped reads, as well as constructing phylogenetic trees of related cells. We validate Ginkgo by reproducing the results of five major studies and examine the data characteristics of three commonly used single-cell amplification techniques to conclude DOP-PCR to be the most consistent for CNV analysis.

Bioinformatics↗

bioRxiv2014

Error correction and assembly complexity of single molecule sequencing reads.

Third generation single molecule sequencing technology is poised to revolutionize genomics by enabling the sequencing of long, individual molecules of DNA and RNA. These technologies now routinely produce reads exceeding 5,000 basepairs, and can achieve reads as long as 50,000 basepairs. Here we evaluate the limits of single molecule sequencing by assessing the impact of long read sequencing in the assembly of the human genome and 25 other important genomes across the tree of life. From this, we develop a new data-driven model using support vector regression that can accurately predict assembly performance. We also present a novel hybrid error correction algorithm for long PacBio sequencing reads that uses pre-assembled Illumina sequences for the error correction. We apply it several prokaryotic and eukaryotic genomes, and show it can achieve near-perfect assemblies of small genomes (< 100Mbp) and substantially improved assemblies of larger ones. All source code and the assembly model are available open-source.

Bioinformatics↗

bioRxiv2014

SplitMEM: Graphical pan-genome analysis with suffix skips

Motivation: With the rise of improved sequencing technologies, genomics is expanding from a single reference per species paradigm into a more comprehensive pan-genome approach with multiple individuals represented and analyzed together. One of the most sophisticated data structures for representing an entire population of genomes is a compressed de Bruijn graph. The graph structure can robustly represent simple SNPs to complex structural variations far beyond what can be done from linear sequences alone. As such there is a strong need to develop algorithms that can efficiently construct and analyze these graphs. Results: In this paper we explore the deep topological relationships between the suffix tree and the compressed de Bruijn graph. We introduce a novel O(n log n) time and space algorithm called splitMEM, that directly constructs the compressed de Bruijn graph for a pan-genome of total length n. To achieve this time complexity, we augment the suffix tree with suffix skips, a new construct that allows us to traverse several suffix links in constant time, and use them to efficiently decompose maximal exact matches (MEMs) into the graph nodes. We demonstrate the utility of splitMEM by analyzing the pan- genomes of 9 strains of Bacillus anthracis and 9 strains of Escherichia coli to reveal the properties of their core genomes. Availability: The source code and documentation are available open- source at http://splitmem.sourceforge.net

Bioinformatics↗