Donna K. Slonim

An STS-Based Map of the Human Genome

A physical map has been constructed of the human genome containing 15,086 sequence-tagged sites (STSs), with an average spacing of 199 kilobases. The project involved assembly of a radiation hybrid map of the human genome containing 6193 loci and incorporated a genetic linkage map of the human genome containing 5264 loci. This information was combined with the results of STS-content screening of 10,850 loci against a yeast artificial chromosome library to produce an integrated map, anchored by the radiation hybrid and genetic maps. The map provides radiation hybrid coverage of 99 percent and physical coverage of 94 percent of the human genome. The map also represents an early step in an international project to generate a transcript map of the human genome, with more than 3235 expressed sequences localized. The STSs in the map provide a scaffold for initiating large-scale sequencing of the human genome.

From patterns to pathways: gene expression data analysis comes of age

Many different biological questions are routinely studied using transcriptional profiling on microarrays. A wide range of approaches are available for gleaning insights from the data obtained from such experiments. The appropriate choice of data-analysis technique depends both on the data and on the goals of the experiment. This review summarizes some of the common themes in microarray data analysis, including detection of differential expression, clustering, and predicting sample characteristics. Several approaches to each problem, and their relative merits, are discussed and key areas for additional research highlighted.

Composition and dynamics of the Caenorhabditis elegans early embryonic transcriptome.

Temporal profiles of transcript abundance during embryonic development were obtained by whole-genome expression analysis from precisely staged C. elegans embryos. The result is a highly resolved time course that commences with the zygote and extends into mid-gastrulation, spanning the transition from maternal to embryonic control of development and including the presumptive specification of most major cell fates. Transcripts for nearly half (8890) of the predicted open reading frames are detected and expression levels for the majority of them (>70%) change over time. The transcriptome is stable up to the four-cell stage where it begins rapidly changing until the rate of change plateaus before gastrulation. At gastrulation temporal patterns of maternal degradation and embryonic expression intersect indicating a mid-blastula transition from maternal to embryonic control of development. In addition, we find that embryonic genes tend to be expressed transiently on a time scale consistent with developmental decisions being made with each cell cycle. Furthermore, overall rates of synthesis and degradation are matched such that the transcriptome maintains a steady-state frequency distribution. Finally, a versatile analytical platform based on cluster analysis and developmental classification of genes is provided.

Class prediction and discovery using gene expression data

Classification of patient samples is a crucial aspect of cancer diagnosis and treatment. We present a method for classifying samples by computational analysis of gene expression data. We consider the classification problem in two parts: class discovery and class prediction. Class discovery refers to the process of dividing samples into reproducible classes that have similar behavior or properties, while class prediction places new samples into already known classes. We describe a method for performing class prediction and illustrate its strength by correctly classifying bone marrow and blood samples from acute leukemia patients. We also describe how to use our predictor to validate newly discovered classes, and we demonstrate how this technique could have discovered the key distinctions among leukemias if they were not already known. This proof-of-concept experiment paves the way for a wealth of future work on the molecular classification and understanding of disease.

The power of team exploration: two robots can learn unlabeled directed graphs

We show that two cooperating robots can learn exactly any strongly-connected directed graph with n indistinguishable nodes in expected time polynomial in n. We introduce a new type of homing sequence for two robots which helps the robots recognize certain previously-seen nodes. We then present an algorithm in which the robots learn the graph and the homing sequence simultaneously by wandering actively through the graph. Unlike most previous learning results using homing sequences, our algorithm does not require a teacher to provide counterexamples. Furthermore, the algorithm can use efficiently any additional information available that distinguishes nodes. We also present an algorithm in which the robots learn by taking random walks. The rate at which a random walk converges to the stationary distribution is characterized by the conductance of the graph. Our random-walk algorithm learns in expected time polynomial in n and in the inverse of the conductance and is more efficient than the homing-sequence algorithm for high-conductance graphs.<<ETX>>

Toward the dynamic interactome: it's about time

Dynamic molecular interactions play a central role in regulating the functioning of cells and organisms. The availability of experimentally determined large-scale cellular networks, along with other high-throughput experimental data sets that provide snapshots of biological systems at different times and conditions, is increasingly helpful in elucidating interaction dynamics. Here we review the beginnings of a new subfield within computational biology, one focused on the global inference and analysis of the dynamic interactome. This burgeoning research area, which entails a shift from static to dynamic network analysis, promises to be a major step forward in our ability to model and reason about cellular function and behavior.

Building human genome maps with radiation hybrids

Genome maps are crucial tools in human genetic research, providing known landmarks for locating disease genes and frameworks for large-scale sequencing. Radiation hybrid mapping is one technique for building genome maps. In this paper, we describe the methods used to build radiation hybrid maps of the entire human genome. We present the hidden Markov model that we employ to estimate the likelihood of a map despite uncertainty about the data, and we discuss the problem of searching for maximum-likelihood maps. We describe the graph algorithms used to find sparse but reliable initial maps and our methods of extending them. Finally, we show results validating our software on simulated data, and we describe our genome-wide human radiation hybrid maps and the evidence supporting them.

Radiation hybrid map of the mouse genome.

Radiation hybrid (RH) maps are a useful tool for genome analysis, providing a direct method for localizing genes and anchoring physical maps and genomic sequence along chromosomes. The construction of a comprehensive RH map for the human genome has resulted in gene maps reflecting the location of more than 30,000 human genes. Here we report the first comprehensive RH map of the mouse genome. The map contains 2,486 loci screened against an RH panel of 93 cell lines. Most loci (93%) are simple sequence length polymorphisms (SSLPs) taken from the mouse genetic map, thereby providing direct integration between these two key maps. We performed RH mapping by a new and efficient approach in which we replaced traditional gel- or hybridization-based assays by a homogeneous 5´-nuclease assay involving a single common probe for all genetic markers. The map provides essentially complete connectivity and coverage across the genome, and good resolution for ordering loci, with 1 centiRay (cR) corresponding to an average of approximately 100 kb. The RH map, together with an accompanying World-Wide Web server, makes it possible for any investigator to rapidly localize sequences in the mouse genome. Together with the previously constructed genetic map and a YAC-based physical map reported in a companion paper, the fundamental maps required for mouse genomics are now available.

The homeodomain protein PAL-1 specifies a lineage-specific regulatory network in the C. elegans embryo

Maternal and zygotic activities of the homeodomain protein PAL-1 specify the identity and maintain the development of the multipotent C blastomere lineage in the C. elegans embryo. To identify PAL-1 regulatory target genes, we used microarrays to compare transcript abundance in wild-type embryos with mutant embryos lacking a C blastomere and to mutant embryos with extra C blastomeres. pal-1-dependent C-lineage expression was verified for select candidate target genes by reporter gene analysis, though many of the target genes are expressed in additional lineages as well. The set of validated target genes includes 12 transcription factors, an uncharacterized wingless ligand and five uncharacterized genes. Phenotypic analysis demonstrates that the identified PAL-1 target genes affect specification, differentiation and morphogenesis of C-lineage cells. In particular, we show that cell fate-specific genes (or tissue identity genes) and a posterior HOX gene are activated in lineage-specific fashion. Transcription of targets is initiated in four temporal phases, which together with their spatial expression patterns leads to a model of the regulatory network specified by PAL-1.

Functional genomic analysis of amniotic fluid cell-free mRNA suggests that oxidative stress is significant in Down syndrome fetuses

To characterize the differences between second trimester Down syndrome (DS) and euploid fetuses, we used Affymetrix microarrays to compare gene expression in uncultured amniotic fluid supernatant samples. Functional pathway analysis highlighted the importance of oxidative stress, ion transport, and G protein signaling in the DS fetuses. Further evidence supporting these results was derived by correlating the observed gene expression patterns to those of small molecule drugs via the Connectivity Map. Our results suggest that there are secondary adverse consequences of DS evident in the second trimester, leading to testable hypotheses about possible antenatal therapy for DS.