3 research outputs found
Microbiome profiling by Illumina sequencing of combinatorial sequence-tagged PCR products
We developed a low-cost, high-throughput microbiome profiling method that
uses combinatorial sequence tags attached to PCR primers that amplify the rRNA
V6 region. Amplified PCR products are sequenced using an Illumina paired-end
protocol to generate millions of overlapping reads. Combinatorial sequence
tagging can be used to examine hundreds of samples with far fewer primers than
is required when sequence tags are incorporated at only a single end. The
number of reads generated permitted saturating or near-saturating analysis of
samples of the vaginal microbiome. The large number of reads al- lowed an
in-depth analysis of errors, and we found that PCR-induced errors composed the
vast majority of non-organism derived species variants, an ob- servation that
has significant implications for sequence clustering of similar high-throughput
data. We show that the short reads are sufficient to assign organisms to the
genus or species level in most cases. We suggest that this method will be
useful for the deep sequencing of any short nucleotide region that is
taxonomically informative; these include the V3, V5 regions of the bac- terial
16S rRNA genes and the eukaryotic V9 region that is gaining popularity for
sampling protist diversity.Comment: 28 pages, 13 figure
Cleaning inconsistencies in information extraction via prioritized repairs
The population of a predefined relational schema from textual content, commonly known as Information Extraction (IE), is a pervasive task in contemporary computational challenges associated with Big Data. Since the textual content varies widely in nature and structure (from machine logs to informal natural language), it is notoriously difficult to write IE programs that extract the sought information without any inconsistencies (e.g. a substring should not be annotated as both an address and a person name). Dealing with inconsistencies is hence of crucial importance in IE systems. Industrial-strength IE systems like GATE and IBM SystemT therefore provide a built-in collection of cleaning operations to remove inconsistencies from extracted relations. These operations, however, are collected in an ad-hoc fashion through use cases. Ideally, we would like to allow IEdevelopers to declare their own policies. But existing cleaning operations are defined in an algorithmic way and, hence, it is not clear how to extend the built-in operations without requiring low-level coding of internal or external functions.We embark on the establishment of a framework for declarative cleaning of inconsistencies in IE, though principles of database theory. Specifically, building upon the formalism of document spanners for IE, we adopt the concept of prioritized repairs, which has been recently proposed as an extension of the traditional database repairs to incorporate priorities among conflicting facts. We show that our framework captures the popular cleaning policies, as well as the POSIX semantics for extraction through regular expressions. We explore the problem of determining whether a cleaning declaration is unambiguous (i.e. always results in a single repair), and whether it increases the expressive power of the extraction language. We give both positive and negative results, some of which are general, and some of which apply to policies used in practice.info:eu-repo/semantics/publishe