Katherine S. Pollard

Bio: Katherine S. Pollard is an academic researcher from University of California, San Francisco. The author has contributed to research in topics: Genome & Microbiome. The author has an hindex of 75, co-authored 331 publications receiving 38006 citations. Previous affiliations of Katherine S. Pollard include University of California, Davis & Indiana University.

Structure, function and diversity of the healthy human microbiome

Abstract: The Human Microbiome Project Consortium reports the first results of their analysis of microbial communities from distinct, clinically relevant body habitats in a human cohort; the insights into the microbial communities of a healthy population lay foundations for future exploration of the epidemiology, ecology and translational applications of the human microbiome.

Structure, function and diversity of the healthy human microbiome

Abstract: Studies of the human microbiome have revealed that even healthy individuals differ remarkably in the microbes that occupy habitats such as the gut, skin and vagina. Much of this diversity remains unexplained, although diet, environment, host genetics and early microbial exposure have all been implicated. Accordingly, to characterize the ecology of human-associated microbial communities, the Human Microbiome Project has analysed the largest cohort and set of distinct, clinically relevant body habitats so far. We found the diversity and abundance of each habitat’s signature microbes to vary widely even among healthy subjects, with strong niche specialization both within and among individuals. The project encountered an estimated 81–99% of the genera, enzyme families and community configurations occupied by the healthy Western microbiome. Metagenomic carriage of metabolic pathways was stable among individuals despite variation in community structure, and ethnic/racial background proved to be one of the strongest associations of both pathways and microbes with clinical metadata. These results thus delineate the range of structural and functional configurations normal in the microbial communities of a healthy population, enabling future characterization of the epidemiology, ecology and translational applications of the human microbiome.

Initial sequence of the chimpanzee genome and comparison with the human genome

Abstract: Here we present a draft genome sequence of the common chimpanzee (Pan troglodytes). Through comparison with the human genome, we have generated a largely complete catalogue of the genetic differenc ...

A framework for human microbiome research

Abstract: A variety of microbial communities and their genes (microbiome) exist throughout the human body, playing fundamental roles in human health and disease. The NIH funded Human Microbiome Project (HMP) Consortium has established a population-scale framework which catalyzed significant development of metagenomic protocols resulting in a broad range of quality-controlled resources and data including standardized methods for creating, processing and interpreting distinct types of high-throughput metagenomic data available to the scientific community. Here we present resources from a population of 242 healthy adults sampled at 15 to 18 body sites up to three times, which to date, have generated 5,177 microbial taxonomic profiles from 16S rRNA genes and over 3.5 Tb of metagenomic sequence. In parallel, approximately 800 human-associated reference genomes have been sequenced. Collectively, these data represent the largest resource to date describing the abundance and variety of the human microbiome, while providing a platform for current and future studies.

Detection of nonneutral substitution rates on mammalian phylogenies

Abstract: Methods for detecting nucleotide substitution rates that are faster or slower than expected under neutral drift are widely used to identify candidate functional elements in genomic sequences. However, most existing methods consider either reductions (conservation) or increases (acceleration) in rate but not both, or assume that selection acts uniformly across the branches of a phylogeny. Here we examine the more general problem of detecting departures from the neutral rate of substitution in either direction, possibly in a clade-specific manner. We consider four statistical, phylogenetic tests for addressing this problem: a likelihood ratio test, a score test, a test based on exact distributions of numbers of substitutions, and the genomic evolutionary rate profiling (GERP) test. All four tests have been implemented in a freely available program called phyloP. Based on extensive simulation experiments, these tests are remarkably similar in statistical power. With 36 mammalian species, they all appear to be capable of fairly good sensitivity with low false-positive rates in detecting strong selection at individual nucleotides, moderate selection in 3-bp elements, and weaker or clade-specific selection in longer elements. By applying phyloP to mammalian multiple alignments from the ENCODE project, we shed light on patterns of conservation/acceleration in known and predicted functional elements, approximate fractions of sites subject to constraint, and differences in clade-specific selection in the primate and glires clades. We also describe new "Conservation" tracks in the UCSC Genome Browser that display both phyloP and phastCons scores for genome-wide alignments of 44 vertebrate species.

DADA2: High-resolution sample inference from Illumina amplicon data

Abstract: We present the open-source software package DADA2 for modeling and correcting Illumina-sequenced amplicon errors (https://github.com/benjjneb/dada2). DADA2 infers sample sequences exactly and resolves differences of as little as 1 nucleotide. In several mock communities, DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetected Lactobacillus crispatus variants.

An integrated encyclopedia of DNA elements in the human genome

Abstract: The human genome encodes the blueprint of life, but the function of the vast majority of its nearly three billion bases is unknown. The Encyclopedia of DNA Elements (ENCODE) project has systematically mapped regions of transcription, transcription factor association, chromatin structure and histone modification. These data enabled us to assign biochemical functions for 80% of the genome, in particular outside of the well-studied protein-coding regions. Many discovered candidate regulatory elements are physically associated with one another and with expressed genes, providing new insights into the mechanisms of gene regulation. The newly identified elements also show a statistical correspondence to sequence variants linked to human disease, and can thereby guide interpretation of this variation. Overall, the project provides new insights into the organization and regulation of our genes and genome, and is an expansive resource of functional annotations for biomedical research.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

UPARSE: highly accurate OTU sequences from microbial amplicon reads

Abstract: Amplified marker-gene sequences can be used to understand microbial community structure, but they suffer from a high level of sequencing and amplification artifacts. The UPARSE pipeline reports operational taxonomic unit (OTU) sequences with ≤1% incorrect bases in artificial microbial community tests, compared with >3% incorrect bases commonly reported by other methods. The improved accuracy results in far fewer OTUs, consistently closer to the expected number of species in a community.