Markus J. Bauer

Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms

DNA sequencing continues to decrease in cost with the Illumina HiSeq2000 generating up to 600 Gb of paired-end 100 base reads in a ten-day run. Here we present a protocol for community amplicon sequencing on the HiSeq2000 and MiSeq Illumina platforms, and apply that protocol to sequence 24 microbial communities from host-associated and free-living environments. A critical question as more sequencing platforms become available is whether biological conclusions derived on one platform are consistent with what would be derived on a different platform. We show that the protocol developed for these instruments successfully recaptures known biological results, and additionally that biological conclusions are consistent across sequencing platforms (the HiSeq2000 versus the MiSeq) and across the sequenced regions of amplicons.

A comprehensive catalogue of somatic mutations from a human cancer genome

All cancers carry somatic mutations. A subset of these somatic alterations, termed driver mutations, confer selective growth advantage and are implicated in cancer development, whereas the remainder are passengers. Here we have sequenced the genomes of a malignant melanoma and a lymphoblastoid cell line from the same person, providing the first comprehensive catalogue of somatic mutations from an individual cancer. The catalogue provides remarkable insights into the forces that have shaped this cancer genome. The dominant mutational signature reflects DNA damage due to ultraviolet light exposure, a known risk factor for malignant melanoma, whereas the uneven distribution of mutations across the genome, with a lower prevalence in gene footprints, indicates that DNA repair has been preferentially deployed towards transcribed regions. The results illustrate the power of a cancer genome sequence to reveal traces of the DNA damage, repair, mutation and selection processes that were operative years before the cancer became symptomatic.

Genome Sequencing and Analysis of the Tasmanian Devil and Its Transmissible Cancer

Summary The Tasmanian devil (Sarcophilus harrisii), the largest marsupial carnivore, is endangered due to a transmissible facial cancer spread by direct transfer of living cancer cells through biting. Here we describe the sequencing, assembly, and annotation of the Tasmanian devil genome and whole-genome sequences for two geographically distant subclones of the cancer. Genomic analysis suggests that the cancer first arose from a female Tasmanian devil and that the clone has subsequently genetically diverged during its spread across Tasmania. The devil cancer genome contains more than 17,000 somatic base substitution mutations and bears the imprint of a distinct mutational process. Genotyping of somatic mutations in 104 geographically and temporally distributed Tasmanian devil tumors reveals the pattern of evolution and spread of this parasitic clonal lineage, with evidence of a selective sweep in one geographical area and persistence of parallel lineages in other populations. PaperClip

Lightweight algorithms for constructing and inverting the BWT of string collections

Recent progress in the field of DNA sequencing motivates us to consider the problem of computing the Burrows-Wheeler transform (BWT) of a collection of strings. A human genome sequencing experiment might yield a billion or more sequences, each 100 characters in length. Such a dataset can now be generated in just a few days on a single sequencing machine. Many algorithms and data structures for compression and indexing of text have the BWT at their heart, and it would be of great interest to explore their applications to sequence collections such as these. However, computing the BWT for 100 billion characters or more of data remains a computational challenge. In this work we address this obstacle by presenting a methodology for computing the BWT of a string collection in a lightweight fashion. A first implementation of our algorithm needs O(mlogm) bits of memory to process m strings, while a second variant makes additional use of external memory to achieve RAM usage that is constant with respect to m and negligible in size for a small alphabet such as DNA. The algorithms work on any number of strings and any size. We evaluate our algorithms on collections of up to 1 billion strings and compare their performance to other approaches on smaller datasets. We take further steps toward making the BWT a practical tool for processing string collections on this scale. First, we give two algorithms for recovering the strings in a collection from its BWT. Second, we show that if sequences are added to or removed from the collection, then the BWT of the original collection can be efficiently updated to obtain the BWT of the revised collection.

Lightweight BWT construction for very large string collections

A modern DNA sequencing machine can generate a billion or more sequence fragments in a matter of days. The many uses of the BWT in compression and indexing are well known, but the computational demands of creating the BWT of datasets this large have prevented its applications from being widely explored in this context. We address this obstacle by presenting two algorithms capable of computing the BWT of very large string collections. The algorithms are lightweight in that the first needs O(m log m) bits of memory to process m strings and the memory requirements of the second are constant with respect to m. We evaluate our algorithms on collections of up to 1 billion strings and compare their performance to other approaches on smaller datasets. Although our tests were on collections of DNA sequences of uniform length, the algorithms themselves apply to any string collection over any alphabet.